{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wholesale customers dataset has 440 samples with 6 features each.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Show matplotlib plots inline (nicely formatted in the notebook)\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the wholesale customers dataset\n",
    "try:\n",
    "    data = pd.read_csv(\"Wholesale.csv\")\n",
    "    data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n",
    "    print(\"Wholesale customers dataset has {} samples with {} features each.\".format(*data.shape))\n",
    "except:\n",
    "    print(\"Dataset could not be loaded. Is the dataset missing?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper    Delicassen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the features of the data, we started segmenting our customers by common sense. For example, what type of businesses purchase large amount of milk and grocery (retailer)? What type of businesses purchase large amount of fresh, frozen and detergents paper (restaurant)? What type of businesses purchase large amount of delicassen (deli)? Let's do this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0  12669  9656     7561     214              2674        1338\n",
       "1   7057  9810     9568    1762              3293        1776\n",
       "2   6353  8808     7684    2405              3516        7844\n",
       "3  13265  1196     4221    6404               507        1788\n",
       "4  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting samples\n",
    "\n",
    "To get a better understanding of the customers and how their data will transform through the analysis, we will select a few sample data points according to the above common sense segmentation and explore them in more detail. The selected samples should vary significantly from one another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fresh_q1 = 3127.75\n",
    "milk_q1 = 1533\n",
    "grocery_q1 = 2153\n",
    "frozen_q1 = 742.25\n",
    "deter_q1 = 256.75\n",
    "deli_q1 = 408.25\n",
    "\n",
    "fresh_q3 = 16933.75\n",
    "milk_q3 = 7190.25\n",
    "grocery_q3 = 10655.75\n",
    "frozen_q3 = 3554.25\n",
    "deter_q3 = 3922\n",
    "deli_q3 = 1820.25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### High milk, high grocery, low delicassen and low fresh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
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       "      <th>Frozen</th>\n",
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       "      <th>Delicassen</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>630</td>\n",
       "      <td>11095</td>\n",
       "      <td>23998</td>\n",
       "      <td>787</td>\n",
       "      <td>9529</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>491</td>\n",
       "      <td>10473</td>\n",
       "      <td>11532</td>\n",
       "      <td>744</td>\n",
       "      <td>5611</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>219</td>\n",
       "      <td>9540</td>\n",
       "      <td>14403</td>\n",
       "      <td>283</td>\n",
       "      <td>7818</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1406</td>\n",
       "      <td>16729</td>\n",
       "      <td>28986</td>\n",
       "      <td>673</td>\n",
       "      <td>836</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>355</td>\n",
       "      <td>7704</td>\n",
       "      <td>14682</td>\n",
       "      <td>398</td>\n",
       "      <td>8077</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>260</td>\n",
       "      <td>8675</td>\n",
       "      <td>13430</td>\n",
       "      <td>1116</td>\n",
       "      <td>7015</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "43     630  11095    23998     787              9529          72\n",
       "53     491  10473    11532     744              5611         224\n",
       "81     219   9540    14403     283              7818         156\n",
       "109   1406  16729    28986     673               836           3\n",
       "159    355   7704    14682     398              8077         303\n",
       "170    260   8675    13430    1116              7015         323"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.Grocery > grocery_q3) & (data.Milk > milk_q3) & (data.Delicassen < deli_q1) & (data.Fresh < fresh_q1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### High fresh, high frozen and low grocery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
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       "      <th>Frozen</th>\n",
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       "      <th>39</th>\n",
       "      <td>56159</td>\n",
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       "      <th>75</th>\n",
       "      <td>20398</td>\n",
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       "      <td>3</td>\n",
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       "      <th>126</th>\n",
       "      <td>19219</td>\n",
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       "      <td>349</td>\n",
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       "      <th>190</th>\n",
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       "      <td>7332</td>\n",
       "      <td>118</td>\n",
       "      <td>64</td>\n",
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       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>18692</td>\n",
       "      <td>3838</td>\n",
       "      <td>593</td>\n",
       "      <td>4634</td>\n",
       "      <td>28</td>\n",
       "      <td>1215</td>\n",
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       "    <tr>\n",
       "      <th>258</th>\n",
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       "      <td>2124</td>\n",
       "      <td>6422</td>\n",
       "      <td>730</td>\n",
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       "      <th>273</th>\n",
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       "      <td>3045</td>\n",
       "      <td>1493</td>\n",
       "      <td>4802</td>\n",
       "      <td>210</td>\n",
       "      <td>1824</td>\n",
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       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>25767</td>\n",
       "      <td>3613</td>\n",
       "      <td>2013</td>\n",
       "      <td>10303</td>\n",
       "      <td>314</td>\n",
       "      <td>1384</td>\n",
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       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>20105</td>\n",
       "      <td>1887</td>\n",
       "      <td>1939</td>\n",
       "      <td>8164</td>\n",
       "      <td>716</td>\n",
       "      <td>790</td>\n",
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       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>28257</td>\n",
       "      <td>944</td>\n",
       "      <td>2146</td>\n",
       "      <td>3881</td>\n",
       "      <td>600</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>17770</td>\n",
       "      <td>4591</td>\n",
       "      <td>1617</td>\n",
       "      <td>9927</td>\n",
       "      <td>246</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>27167</td>\n",
       "      <td>2801</td>\n",
       "      <td>2128</td>\n",
       "      <td>13223</td>\n",
       "      <td>92</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "39   56159   555      902   10002               212        2916\n",
       "75   20398  1137        3    4407                 3         975\n",
       "126  19219  1840     1658    8195               349         483\n",
       "190  16936  6250     1981    7332               118          64\n",
       "237  18692  3838      593    4634                28        1215\n",
       "258  56083  4563     2124    6422               730        3321\n",
       "273  36817  3045     1493    4802               210        1824\n",
       "283  25767  3613     2013   10303               314        1384\n",
       "371  20105  1887     1939    8164               716         790\n",
       "380  28257   944     2146    3881               600         270\n",
       "381  17770  4591     1617    9927               246         532\n",
       "401  27167  2801     2128   13223                92        1902\n",
       "436  39228  1431      764    4510                93        2346"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.Fresh > fresh_q3) & (data.Frozen > frozen_q3) & (data.Grocery < grocery_q1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### High delicassen, low frozen and low fresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1454</td>\n",
       "      <td>6337</td>\n",
       "      <td>10704</td>\n",
       "      <td>133</td>\n",
       "      <td>6830</td>\n",
       "      <td>1831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>1989</td>\n",
       "      <td>10690</td>\n",
       "      <td>19460</td>\n",
       "      <td>233</td>\n",
       "      <td>11577</td>\n",
       "      <td>2153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>200</td>\n",
       "      <td>25862</td>\n",
       "      <td>19816</td>\n",
       "      <td>651</td>\n",
       "      <td>8773</td>\n",
       "      <td>6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>834</td>\n",
       "      <td>11577</td>\n",
       "      <td>11522</td>\n",
       "      <td>275</td>\n",
       "      <td>4027</td>\n",
       "      <td>1856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>3062</td>\n",
       "      <td>6154</td>\n",
       "      <td>13916</td>\n",
       "      <td>230</td>\n",
       "      <td>8933</td>\n",
       "      <td>2784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>161</td>\n",
       "      <td>7460</td>\n",
       "      <td>24773</td>\n",
       "      <td>617</td>\n",
       "      <td>11783</td>\n",
       "      <td>2410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>1479</td>\n",
       "      <td>14982</td>\n",
       "      <td>11924</td>\n",
       "      <td>662</td>\n",
       "      <td>3891</td>\n",
       "      <td>3508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>3097</td>\n",
       "      <td>4230</td>\n",
       "      <td>16483</td>\n",
       "      <td>575</td>\n",
       "      <td>241</td>\n",
       "      <td>2080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "106   1454   6337    10704     133              6830        1831\n",
       "155   1989  10690    19460     233             11577        2153\n",
       "171    200  25862    19816     651              8773        6250\n",
       "189    834  11577    11522     275              4027        1856\n",
       "245   3062   6154    13916     230              8933        2784\n",
       "304    161   7460    24773     617             11783        2410\n",
       "315   1479  14982    11924     662              3891        3508\n",
       "430   3097   4230    16483     575               241        2080"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.Delicassen > deli_q3) & (data.Frozen < frozen_q1) & (data.Fresh < fresh_q1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample 1: High on milk and grocery, I guess it is a retailer. \n",
    "### Sample 2: High on fresh, frozen and detergents paper, I guess it is a restaurant.\n",
    "### Sample 3: High on delicassen, I guess it is a deli."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen samples of wholesale customers dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1406</td>\n",
       "      <td>16729</td>\n",
       "      <td>28986</td>\n",
       "      <td>673</td>\n",
       "      <td>836</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25767</td>\n",
       "      <td>3613</td>\n",
       "      <td>2013</td>\n",
       "      <td>10303</td>\n",
       "      <td>314</td>\n",
       "      <td>1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1479</td>\n",
       "      <td>14982</td>\n",
       "      <td>11924</td>\n",
       "      <td>662</td>\n",
       "      <td>3891</td>\n",
       "      <td>3508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0   1406  16729    28986     673               836           3\n",
       "1  25767   3613     2013   10303               314        1384\n",
       "2   1479  14982    11924     662              3891        3508"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indices = [109, 283, 315]\n",
    "\n",
    "samples = pd.DataFrame(data.loc[indices], columns = data.columns).reset_index(drop = True)\n",
    "print(\"Chosen samples of wholesale customers dataset:\")\n",
    "display(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_mean = data.describe().loc['mean',:]\n",
    "samples_bar = samples.append(data_mean)\n",
    "samples_bar.index = indices + ['mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138859516d8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ugJ8ACzPzuw0O3Q2s2UFuLHBXg/YvlXah2x9YUVpedx8wIiJ2KG2wMAK4r3Ts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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13883eae2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "samples_bar.plot(kind='bar', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this comparison plot, we can see that our three samples are very different with mean, and very different with each other.\n",
    "\n",
    "Sample 1 represents a retailer, high purchase on grocery and milk; sample 2 represents a restaurant, high purchase on fresh and frozen; sample 3 represents a deli, high purchase on delicassen and detergents paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature relevance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.\n",
    "In the code block below, you will need to implement the following:\n",
    "* Assign new_data a copy of the data by removing a feature of your choice using the DataFrame.drop function.\n",
    "* Use sklearn.cross_validation.train_test_split to split the dataset into training and testing sets. Use the removed feature as your target label. Set a test_size of 0.25 and set a random_state.\n",
    "* Import a decision tree regressor, set a random_state, and fit the learner to the training data.\n",
    "* Report the prediction score of the testing set using the regressor's score function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score for Fresh as dependent variable: -0.38574971020407384\n",
      "R2 score for Milk as dependent variable: 0.15627539501732116\n",
      "R2 score for Grocery as dependent variable: 0.6818840085440834\n",
      "R2 score for Frozen as dependent variable: -0.21013589012491396\n",
      "R2 score for Detergents_Paper as dependent variable: 0.27166698062685013\n",
      "R2 score for Delicassen as dependent variable: -2.254711537203931\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Create list to loop through\n",
    "dep_vars = list(data.columns)\n",
    "\n",
    "\n",
    "# Create loop to test each feature as a dependent variable\n",
    "for var in dep_vars:\n",
    "\n",
    "    # TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n",
    "    new_data = data.drop([var], axis = 1)\n",
    "    # Confirm drop\n",
    "    # display(new_data.head(2))\n",
    "\n",
    "    # Create feature Series (Vector)\n",
    "    new_feature = pd.DataFrame(data.loc[:, var])\n",
    "    # Confirm creation of new feature\n",
    "    # display(new_feature.head(2))\n",
    "\n",
    "    # TODO: Split the data into training and testing sets using the given feature as the target\n",
    "    X_train, X_test, y_train, y_test = train_test_split(new_data, new_feature, test_size=0.25, random_state=42)\n",
    "\n",
    "    # TODO: Create a decision tree regressor and fit it to the training set\n",
    "    # Instantiate\n",
    "    dtr = DecisionTreeRegressor(random_state=42)\n",
    "    # Fit\n",
    "    dtr.fit(X_train, y_train)\n",
    "\n",
    "    # TODO: Report the score of the prediction using the testing set\n",
    "    # Returns R^2\n",
    "    score = dtr.score(X_test, y_test)\n",
    "    print('R2 score for {} as dependent variable: {}'.format(var, score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Which feature did you attempt to predict? What was the reported prediction score? Is this feature is necessary for identifying customers' spending habits?\n",
    "\n",
    "Hint: The coefficient of determination, R^2, is scored between 0 and 1, with 1 being a perfect fit. A negative R^2 implies the model fails to fit the data.\n",
    "\n",
    "### Answer:\n",
    "I used a loop and predicted every single feature as a dependent variable with the results shown above.\n",
    "As you can see, \"Fresh\", \"Frozen\" and \"Delicassen\" as dependent variables have negative R2 scores.\n",
    "Their negative scores imply that they are necessary for identifying customers' spending habits because the remaining features cannot explain the variation in them.\n",
    "\n",
    "Similarly, \"Milk\" and \"Detergents_Paper\" have very low R2 scores.\n",
    "Their low scores also imply that they are necessary for identifying customers' spending habits.\n",
    "\n",
    "However, \"Grocery\" has a R2 score of 0.68.\n",
    "It is relative to the others it is much higher.\n",
    "It may be not as necessary, compared to the other features, for identifying customers' spending habits.\n",
    "We will explore this further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Feature Distributions\n",
    "To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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9K3V0PX5o13vecnqKG0SMF1K0bZ9iOrHiRvzEeIFC2mQ0n3qsN2gp5bZ4IKyH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brtc2Ioc91kRLEWpVOUGi5BKGnYPpdWGmgCTo9lQUQ3Ca4f0naD7sw5wPRqk6WazeH+JKO5xHUr\nYvvlmhuEksVKm/eLdWZKTYJQ4gYhf+70AQ7epHpMTNcITbW5vZds5or2h0TlCv8UONv588pWDOpe\n0rSo8oUbhrw0XWaq3OBCsUEyZrBQ/WCH/8WVOpm4wWrT5dTBHPePZrtLQMrudvZKmXOzVb57YeWm\nzx/uT5GM6Yzk4qw2bC6vNLC9kKFMnJgh+MihfHc2vNryeHGqRNsNyMYNHhjNbmgMl4pNGrbP9GoT\n2wu27Hfbr/76kxMsVG2+fr64/ouVe+Kz9w0BEtsNmS61KDVdlms2Z6+s4fjqGN/tNC06t00MJDk9\ntjXdai8s13nuYolvvVdktrOR/fLq9aV8BzMWQsDEQApdExTSMSxTI5+MbuRODGcoNz3eXazzypW1\n7s+FnXSohu1zsbh/ywMbmmCpZuMFIXanoVE+YWIY4obKKFJKzl5Z47mLJc4vrl8tTNkd7nhGXAjx\n54ExKeWvdL5/CRgEJPAzWzu8rVVqOHzz3WW+d2mV+4ezOH5IvR3gB5JAyuvKKRVSFmEIp8esm951\nKruTlJKW6/Pech1DExwfznSbHSxVbaZWG+QSMT56pIDrh3zt/DLzazaOH1BIZXhk/Pr23NW2RxhG\nFVgGs3FSG1wKHUjHaDo+2YRJTM3gbtrn7h/iYD7B//nsZb7v1PANFQOUe+/yapOmE2D7AcPZOLlU\njLbrU6zZfPfCKp84NqDSUXa5fDJ23flts67WlK+1PeYqbSxDcLh/uPv8UtVmttxCCBjszGYfzCdY\nzCd5d6lGzNAwDa07Ux/ID2ZyNU3Ql4qx1nTpT+3fvSEhEDM1PF9yIBcHBG0/4PdfX+T+0SzHhj5o\nQOeHklpnk+baXdRpV3bG3aSm/APgr1zzfQx4DEgD/zfwW1swrnuibns8d6nEXLlNreXy4ME8KUvn\nscPDPH64cF1JpYfHctheSHwDNXNv9u+YuqYuSvdIteXRdH1GsvHuzPVVQghODmco1hz6kjGaToDj\nBby7WOOdxSpLNRddE1xcqROGsFhpc+pAhpFcgtNj+RtymkfzcdZaLqGUHMhvfEXk+HDU/SymazeM\n8Vq2FxCEcsMB/n5l6BpffnqS/+n33uaFy2WePNq/00PaV6SUvDxdxvZD4rrGSNbirzw2ht3ZzP7a\nTIVETOfUgRypTnk5pTeFoaTu+Lh+iKmLbuAehpIr5SbJmM5wNpqcOj6U5sJytIm3aXvMtlxSlkku\naTKUidNwfCBKi1motmi6MUaycR4/0ocfhhiaRq3t8fB4juWaw1D2+oD70UP5fb/p19AECUOn0vYY\nysQwdI1TB3PMlJqsNhxCKcklTPpSUerssaE0xbrDxMC9a64VhJJGJ0f/dtc3ZWPu5uofk1LOXvP9\nd6WUZaAshNjV23QdL+T9pTqlpkszYTCYttGEoOlE6Qd12+ORQ1H7ZiHEdblq1yrWbdpuwNhNShXO\nrbV4d7GOrgk+NllQHRa3WMv1+ca7y9hewCPjeR44cP0Sa6nh0HJDTo/l0TXB5GCKP357iQvLDRar\nbfqSUc7/2ek1YrqgbvsYuuATxwZuurHQ1DUeHs8jpeT8Yp1K2+XkcGZD1TvWu3jUbI+z02XCEB4a\ny+2bMlx36y8/Ps4vf+si/+obF1Qgvs0urUQlKeu2R0DIC5dLTJVafPmTR5DAUMbi3YUqs6UW8ZjO\nDzw0iq6C8Z70zXeXuVhsEkjJyeEMjxyK6n6fvbLGsxdWWKnbDGUsTh3M89SxATIJg5iu4YUSP4ia\nzxRrDkOZOIf7k7h+SNP1mV5tYeo2LTfqtXDfSJa1lsvkYJpM3CRzk30latNvVHGmZntUWi6akJQa\nHrYXMJC2mFptsli1O/FGPwJYrtmYhkbfFq18SCmZW2uja6Lb0fS1mTUqLY9COsajh/rW+RuU9dxN\nlHjd/3Up5U9e8+3g5oZzb02tNnCDED+QxA2NREzHDSRvLdSo2z7VtsdoPtFNZZgptbD9gCMDKVw/\nZKbcwtQF06tRC1nbC2+oF11rRzMAQShpOsEtA/Fqy+P8Uo20ZXDqQFYttW9QteV1m4VkE000TTBX\nbnFsKMPh/iTn5qoEocQPQ2KGhh9KWm6Uv5pPmnzy2ABtz2d+rc17izXySZNqy+OthRqfPH7rw7fh\n+Cx0WgtPl1rdQNz1Q66UmiQt445TmJqOT9jZ+1m3b18P1/YC3pqvIoTgwYPZfdkwKG7q/PjTk/zT\nPzzPcxdX+fgxVVd8uyxUbJpOgB+EzJRaSClougFTq00+f2qUN+crvDXXBqJqGHPj7e55dC+IAp42\nE/2pnmyv3nB83lmoYRkaDx7M3bLMbqnh8HanXC8CxvIJvnthlaGMxfnFGit1m/eXGzSdAFPX0QQ8\nfymaxHrqeD/1dlQW9mr3xyCUJGIaF4s2F1fqHBlIIzvpJxMDKSa49THi+iGGJvb9jGsqZtB0fNZa\nLldKTQ7kk4QhLNccBtIxglB2N+Iu12zqdhSDzK218IJotnwzkzwz5RYXlqMcfUMTDGXj3dz0mqpV\n3hWEkrfmq9hewKmDuTuqonc3gfiLQogfk1L+22sfFEL8OPDSXfx928b1JTIMCZG0vQDHDzk6mGax\n0qbScjk3V+XTJ4c4VEhSarrdZhUQ5QpXW9GdqK4JTF3jZrHz5GCqWzN1IH3rO9LpUrPbzXOsL7Gl\neXt7Qc32cP3whg5smYTJxEAS2w0Z70vwxkyF2bU2F1ca/K1PTBIzNNpuFLQ6fkjD8Xnm5CBCSmbW\nWpybr5JPGCzXolkE0xT0pWM4XkgYylue9JMxg5QVnRAHrxnTxWKjG6CnY0a3WkDT8Wm60WtvdZM1\nnIlT6fPwA8l44fbLiItVm0orOuktVx0O9d+7Zcfd7K89cZhfe36an/vdt/ivP/X0lpRHVNaXiRuc\nm690Nh+HIGAgY+GH0YxZKmYwmotzebXB5GDuptWl7sZqI+rfsJMdi8NQcqmzWfDSSqMnA/HZcqsb\nNK02nFsGZkIIxvqSxEyNnGUShCGZuNm9Fi7XHHRgsdomZghMXbDcqYqyWve6N19BKAnDKJ1pYa1N\n1fYZ70vSlzSZ3EC33aWqzdsLVWKGxkePFPblxMNVmgZL1TardQcBVJoOy3UDQdTn5HP3D/HQWI5c\nwsQPQubWWuiaFtUbd6JJqOwxk0RM7zavS5j6TVcgbkZcW6658+WpAznmK+3uDZcCpabTbbg0U2rx\nwIGNFXaAuwvE/x7wO0KIvwq82nnsMcACfugu/r5t8+BYlnwqjh/aZBIxCimL0+M5Lq02EAJGc3EW\nq+3OiUVSajqUGh5eEHZPvpmEyX0jaVxf3nQGNG7q3TrTYSiZW2sRN/UbOgwOZCxW6g6JmK7ygz+k\nbnu8PFVGSq7biAJRve5nTg7RcgMO5BNcKkaz45ah0XR8zkz0UW15tF2fP3pzEQlU2z4nRzO8uVBj\nbq1NMmYwkDZpOBqfODrARH+aXNKgbvu3rAGua4InJgt4gewGf3NrLV6bWaPlBhwZSGLo0VnK9gJe\nmioThFGAfasum5omuH+DVVgKyRjTmgAB+dT+KNt1M3FT5x//4IP8zX//Mr/8zQv8D184udND2hcq\nLZeMZWB7AVJG3YgfO9zHeF+CC8t1TENnvJBkYiDJfaNZRnObv0BfKTW5sBydmx8/UtixYFzrVPoo\nN9w7bs2+W/SnY7yzUCWU3HamrpCK8ckTA5SbLrPlFg3bp9xyiZs6b81XAUkqbpCxDAbTcfKpGMMZ\ni6Slc2QgRShBCDANjbBTkjCXNJmvtKjbHof7N9Z5erXhRLXFvZC67WOl928gjoxSQoQQCAFCg3rL\nIRE3kZg8OJbv3lj1py2ePj6IEIJ3l2o0nQBdF2id++JLK02mV5toGjwx2b+h1NnxQgJdF9FseKdy\n3HA2rlIpPyQbN4kZGl4Q3nYS9mbuOAKUUhaBjwshPgOc6jz8h1LKb97p37XdJgcz/NWPHuI77xcp\n1m2mVxt4QXRhySdjFFIx6rZPve1jGhpSRsG2qWuMFxKM5uI3bXN/K+8t1fjepRJI+KFHD153Ej+Y\nTzCYttTS2024fsjVzfOOf2Pd7v60xdUM4c/eP0z73AK2HzBTbvHweJ50XJKOG4zk4yCjWZuEYbBQ\naVFr+aQsnb6UxaFCkk+fGCKTMHlpqsTCms3JkcwtZ6frjo+UdAPx+bU2Q1mLatvjobF894bKD2W3\nCsBWlXXLJU0+2Wnxvt83wj1z3xB/8dExfulbFzl1MMf3nRrZ6SHteY4fslBpRUGUgP6kyeMTfUyV\nmgxnLT59YgghBANp647PZ8V6VFljOBtnrO+Dz97Vz76U0TlhJ31kPI8bhD07M7tctfnWe0VSlsHJ\nkQynx25sSnbVUCaOJgSXig1SMYPJnMVqw8XUBaWGi6EJBlIWIVF1qM98+mjnvY+xUnewDL170/TQ\nwRwrDYfLK01absALl8s8dqhv3f0Dh/qTNB2fZMygsM9XiwczFg8ezHP2yhqrdbtTgCAkqLmM96WY\n/FAHTTcIsb2Q+0eyZCyTTNzoHrdXr0dhGH2mNvK/VgihKsdtwNVmSqGUd7wieNdTsZ3Ae9cH39da\nqrZ58fIqU6Umnhew1vS4tNLgkfE8949meHyiwDsLVV6dKbPSiDaRHBtMIQTEDZ1k6vr/XXXb47WZ\nCpqAhw7mb5hNLdadbjrBUsW+YTZFLavfXH/a4sRwppuffzu5pBktkQchz19e5Y3ZNWbKTd5ZrFNr\nuTx2uI/Lqw2++W6RlXq02czQo3rhE/0pEjGdtht0c7Wbrn/Tf2e14fD6TAWA0+M5BlIWB/IJmkWf\nY0MZhq5Z8UhbBvcfyFK3PSa2ME92vwfg1/pnf+FBLhbr/OR/epV//qXT/MXHxnZ6SHvaYrVFsebg\n+pKYIUAT/JdX5mi6IQlTYGgaz9w3vKEg/MMpYO8t1XG8kErL40Au0X3u6mffMrQdn4kWQvRsEA7w\n3KVVlqoO4PD+cu22gThEKyBL1TYtL2C2olOsOnzv0iordZtM3OTkSAYhYXq1xWguzlzZZrw/wf0j\n2esmT4aycYaycc4v1pgptcgmDG6a0/kh2bjJxybVhmyIrklvzVdp2S62F9JyfHyiAH2p1ubXnpvm\nwYNZHh7vww8kL16OVmOziWiVVxOCRw/3kUuYHBtKY2gayZiu0mHvAV0TN3Re34h9lRNxdnqNc/MV\nlqsOjh8gRHQBWanbTA5molJyUpCMGcQNn4ylU3d8jgykeW+5zgMHPtgkV2o4fPv9FSpNl7YbMLfW\n5mOT/dcFjqcO5liqRjuYU5bOt98rEjd1Hj3Ut6NBuOMHuH644RyxnbDRHOiLxToXiw2Wq228MOBS\nsUXT8Sg1XPqSFt+9WAYBfhAipSSbMDmQTzCaT/DooahmuJSSiYEUM6UmM6UWjhdyeiyHEFGXzXPz\n1U6OqCSXiPH6TAVNCCYGknzmvuGbjiuaQVCzCPdK3NT5f/72x/ixXzvL3/+tN/jWe0V+/gdPbaia\njXLnpldb0fmRqCtjre3Rvpo2YAjOL9Q42JckYxmsNBwOFZLX5QJHJV0F7yzWKTdcjg+nu/nEuYRJ\n0XPIJszrAnRT1zgxfPO0LuXm6rbH67MV9E7wdXX1dnIwxVDGotL2eH+pzn984QqPHurr5rFe7Sas\naYKVus2/e/Yy55fq9CVN4oZOPmniegFBCGtNj6mVBv2ZBHU32sSejBndfU+1ts9ILs6DBz+oaPX5\nB0aYW2sxmLE2lJqifOD1mQrfuxjdSNmeRDeiToyeF1JuuLwxV2Gh0ma+YvOxIwX8IOTlK2vMr7U5\nVIiuc9WWRy5hYhn6LVMllZ2zrwJxyxBU20FUrUKCrksMTes0JZCUmh5JU+fUwSwj+Th12yMMJPOV\nNtOrTebX2nhhyGgugesHmJpgreUBkkzc4KWpEsWazWDa4kq5RTKm88XTowgh+O7FVUqNqKRTpeUy\ntEP5VbYX8MLlEn4gOTGc6blNf34QslCxycQNqm2PX/nWRWZKLdwgRAOqtochIADmay0yMYO2HxDT\nosYRhVSM8UKCj08WODyQIgwl7y3XCUKJZeiEXsBK3cH2og23Dddnte6QiRs03YCDfQlmSy0Q0QbK\nYzIiOQYAACAASURBVEPqpLZTsnGTX//bH+PffOcS/9s3LvC9i6v8oy8+wJcePaiqEG2xIAixO1lW\nngQ3CLAMQRiE6KZJ2w+otT3emq8y0Z9iptzqBuJXc70lEj+Ilm0Xq3Y3EH/oYI7GgE9KlXrdtOWa\ng+NFM9KrDaeb6vOZ+0ZIx0z+4I0FFqsOji/RBAxmYpiaxtfOL7NYtcklDP70/RXOL1SpOz59yRij\n+TjVtteZmIBkLEo9MXSB4wbkOyl5mqBbsWO14Vw3rlzCJJfYmm6e+00QhpRbDg1PIgHfh7gpcPyA\nuu1yfrHGatpivJAklHCwL8F3L65yIG/RdHzynfdQ2b321ZlvIBMnDCVuSPeA1rWQqdUWv/HiDGcm\n+/j0iSGKjYBG26ftBtRsH10XSAmWqWF7IZWmy3LNIRHTGMsn0I1o5tTUNOq2z7m5CrYX0p+OcWwo\nTaXt0XYCijWHwhZ3NrtTbTfqJArc0B63F7y7VGepaiNENItTsz1WGg6GkN0ZOtcLCQFDF5Q8h3TM\nxA8CLFNjdq1Nte1Tbnq4geToUIb5tajqSdLSoxbMqVi3kVOqUwml1vb42JECB/IJdE2wVLW3NO1E\nuTuGrvGTnznO950a4We/8iZ//7fe4Hden+ef/dBDPXeTuZu9OV+97vtS3Wcsn8QyDbLxGON9CTQh\nuu3M+1NWNwXlanAmEOSTBl4gOXzNeyOE2NWrc71kOGuxWG2jd/L1r9I1wX2jWV6drdBYqlFreyzV\nbH7pmxeI6RoLlTZXyi0W1tq0XB8viApkSOnS9gJGshahjM6poYRc0uBgX5KxvgQDmTipmMF4IaoZ\nvlBtc2idKlDKxmUsg0Y74GrPUQm0PUkbSct1yCUCdBF9zrJxk5FsnKdPDvLuYp2PHMrz+JHCTg5f\n2YB9FYgfyMeRyOgEQ3Sicf0QTQhKLYc/eWuJl6fWGOtLko0brDVdUpbB4LgFAoYzFs9dKvPWQpWj\n/UnWWtFJabwvyVghyULF5p2FGnXHIxnTqdnRclDLDUjHDR48mOPRQ/lO3U+5ZbN2i9UokNxIpYK+\nVIyJgSRNJ2BysHcDSTeIbogEkDB11poONdvDvibFO/AlcV2Qimn4Usf1Q0IZEoQhc5UWK3WH02N5\nNC3avHJsMH3DSoWuCR6fKFz3fp0Yzqgl813m+HCG3/rxJ/n1F6/wi3/8Hl/4l9/hH3zfffzoxyfU\nZugtUP/QTXsATK80GM4lOTaU5qnjg1Ra0aRFEET1jB0/4MxEgVzCpG57DGasu15BKtZs3CC8Lodc\nuVEmbt60H0LbDXhnsUqxZnOwL0HD8XlztsJitY0fhtTaPuWWx7V7YjWifUzJmIFpGGgiJBHT0BDM\nrdkUUglCBA+MZq87b6ob4K31/nL9pkULAHwJjufT9gJSMZ2EGXVy/ux9w3z2vmEcP6rgFUrJ6bGc\najC4S+2rd2W55pCNG9TaLl4AhgZBCCGShiNJmIJa22PVbCNEHC8MMXRB0/P5c6cP8MLlEvOVNkjJ\nfNVG1zRcP6TtBVE6S9ZC02CxYtOfjjGUtvj6+WWEiE5WmbjBlVIUACZjOoGU0UaKQ33dLp412+Pt\n+RqWqXH6YO6WG/RsL0qxsb2A84tRjVcp2VCN29tdDP0gJJS7dyPpsaE0MR2++vYy33x3mcVqGw1o\nOMF1QThEN1rZhEHCMsknDSotn5ihETc1dASaEFRaHk9M9q9b1kulOux+mib4kScn+NwDw/zD336L\nf/wH7/BHby7yC196SN04bdLp0QyXS/Z1j5XtEDtoMV6I6kPPlluYus4LUyVMTTCcjZNPmkyvttC0\nGycKvCC8ZXWBIJQIJG4gadge5+aqnZ+R627gVm701bcX+a/nFnh7qYaGhqFBxfbxvABNQIC8IQjP\nJnSGshaPTxQYyMQJAsm33yuCgNF8goGMxadODOxYmuV+Uay3uV3tLTeMZs0TMZ2VhstAOkbbC8jE\nTYo1p1s//s25Kv3pGIcKqTu6vhdrNheKDQqp2IbL7Sp3Zl8F4menVik1Xbww2rgdyih4NU0NS4b4\nUmL7IcjoIvL6TIXVpkvaMvhOfIVvnI9a/1qGxnAuxlg+zWy5xXtLdf7krWUeOZwnpmvcN5pBSskb\n8xUWK9Eu85FslMP1WqfyxuXVJiOdE9hqw4kqcLg+s6UWTcenYUfNEDJxk+PD6et27Lt+2M3ztswP\nPlCSzWm5frf+9emx/A21z3faSt3h1ZkyX3+nyCtXSixU2jcE39eyDGj5kmwQkk3EONyfJm7qeEFI\nXypGonMzpGYJ9pbRXIJ/9zfO8NuvzfPzv/8O3/cv/5QfOH2AH/34YR491Kduqu5C+RYftLYX8tJU\nmR/9v17CNHQeO9yHF4S4ftRZuNx0oxJ4g1F34lTnlPLuUo25cpv+dIyPfKhF9mrD4dxchdlym6GM\nha7B1GqLmKFxZEDNtt6pIJR84/wyz14o0bom2taJalLLkBsCPU1EkzpPHOnniw8fYCgTZ77S5gsP\njLBQbRNKyakDOYazakP6vXap08n7ZgQgJPhSYugCy9D49ntF1loeDx7McaiQxDQ0Wq7PUjXquml7\n4XUbadcztdqk7QbMu+1upTFla+2rCGSp5tCw/e6dvwbE9Oh/gqZp+F6IZsB8pU2t7VKxow1EF1ca\nHB/O4AVRSgkShtJxdA10DUxNo1i3eWu2imVqJC0DP5DEdMHUaoNK26PadhnrS3JiJMNcucV4oY9i\n3UEX0Yfn915fwNQFmbiBpkHDDvDC6GIWM66vHuAFYTfPO20ZTA5GAf2B3OZmJmptv/v3lpvurgzE\np1aa/Om7Syw3bxOBE52gQilImoIAePRQH8O5OJMDaY70J5gqtVmstElcUxN+ptRidq1FIRUjnzSj\nOu+qZGBPEkLwpUfH+PTJIf7ts5f5D89N8/tvLDA5mOKLD43y6ZNDPDKeVxUcNsi7RT18CZ2LfEA+\nZXG52ODEaJZkjM6F3+XyaosvnBohlCHT5RafODrQ2eQOpYZLGEqmStHFfiyf4JvvFik1HBqOTyoW\nVa7qT8ei7sbtqOPubl2x261emS5fF4RDJ/i+RXn2mKmRsUwCoiopQ5k4T3TKCT44pjZdbqeHDub5\nvTeWbvqcJHoLY0LwwHCGF6dW+erbywymLWq2x4HcOE8fH6DlBLw4XSIM6Tae26jhbJy63SCXNLHU\n5+6e2FeB+IFckmuvuyEQSIgJie1LQhmVBNIFLLthZ1OEh+cHfPPdZVpuQNv18YKAc3NVzkz0k4gZ\ntDyXajuqSV5qeji+z6kDOT460U/M0Km327w0VeYrr87xY08f5WA+gZRR1RIhBN88v8zrs2uYusbT\nJwZ4+tggTcfnlZk1XC9ksdLmwnKduKlzfCiqdHJyJEO17TE5mNqyGd2BdIzBjIUXhIwXdt9Mx4F8\nnP/35Zl1g3CIbrJMXZCxTMYKCR47lOfNhRrn7AqWqVGzPQxd492lGiPZOJomuLzawPNDzk6XOXUg\nRyEd49EPzdYpvaWQivEzf+Y+fuKZY/zRuUX+v1fn+NffvsQvffMiuYTJI+N5Hh7P8/BYjhPDGQ7m\nVQ7yzRjazS/AGtHsqe1J1loOSMly3SGbMJjsT/PuUpWmG3B2usTvvjaHEILXrlT47z51FE0EjOTi\nrLVcplaiDrkLlTZxU8MPJaO5OKP5BBOGRrHusFBps1R10LVat3uxsr4glCzU3Nu+RgD9qaj5i0Qw\nnLX45LFB4rHofXeDnW2otJ8Vkre/vgch1Fyf33x1Ds+XtN1oJSoTNzl7pdztSntmokDT8RnO3NmE\n3cRAioN9CQxNqNXEe2RfBeJHBpJkEgZ244NAzgvBc65P6gg+lOPRcEPOTlfQRVS6C2Cl7lJsuBzI\nWmQS0Y5/1w9ZqrYxdcFMucXn7h/i+FCK2XKLhKmjdw7ihuPzypU1pJQcKiR5daZCre2TtHQMTfD6\nbIV8MsaTkwOcX6yyVHU4v1jnyEAq2pHen2S8kGS8M75iLcrd3GyunqFru/oCV2u7TJXa677OEFGp\nSss0ODqU5shAitfnq1Rb0fu+sNamLxXD8VySMaMbeA1l4syWm2Ti0ceifYvmPkrvSVsGP/z4OD/8\n+DjVlsd3LqzwvQurvDFX4Ze/eYFOGWUSps7RoRTHhzIcHUwxmkswnI0zlLXIJ6OuupahEdO1Lbko\nSRlNAGhid+9DWG3dvMJSCN2yhqEr8XwHgUPc1An9gKYbUG55rE6VQQj6kgaXVxu8NV8hFTdBwkrd\nZqbcpD9tcXQgzTuLNeKGxieODXBkICqBWG46GDpoaBvpB7MlwlBSrDukLL2nq7qs9/8rawnuG83x\nY584wvnlJiN5i4RhcHggiaXr6LpQFaJ20B++ubDuayotj5cul0lYOprQ+Oz9Q6QtEynprnJn42a3\n4+mdutNOkcqd6flAXAjxL4AzwKtSyp+63WtTls7aBmZTP+xqYB5eE6B7IcyU2yzXbDIxnULaIh3T\nycQNWq5PueHwrfdW+f5Tw4zmE7x4uUy55VFuOjTsgNW6w2y5xWsza6RMHV2HR8ZzvHC5RCjh/pEs\nB/JxBjNxyk2P/rRJMqbf0H59qWrznfdXCEPJ0ycGONi3d3MoX59a2dDrEqaG0AT3j6bpT1kYmobj\nhfSnTaQUPHgwx0Bn6e7aDZoPHMhyfDhNpeXy3QslbF9wsVhXtcL3mFzS5AcfPsAPPnwAgKbj885i\njYvFBheWG1wo1nnxconffm3+tn9PTNcwdYHZCcxNXcPQBboQaJpAAH4ou6lkfijxw+hrLwjxQ0nQ\nOakIEaW4Xf37rt60R/+JvtZEVELVMqKbgeiPjmVqGFpUYhWi5WrZ+Sb6+oPHueY5iC7SbS/A7v4J\nu98/97Of6TZIMjeQFhpICDpBuRMEnF9uEI9F50Mhr9Y+DskldV6frXDfaI65chtJNIZCyuLIYIqF\nSpt80mR+zaY/bWF7AUOZOB+d6KfW9retJvKFYoPZcrTR9MnJgZ7NjTVus8IzmDT5m09N8AOPHKTa\n8qm5UZm8zz8wRNsLSVmGSgPaYfOl5rqvSXWqtIXAYNZgMB1nrJAkbka9M5TdracDcSHEo0BKSvlJ\nIcT/LoR4XEr58q1e7wbyhtnurWD7EiGgL20xkI2zWLMxRHTy8iUMpOPdmZ0rqy3uP5Cl5fqYhobn\nh5TbLkMZi7ih0XCCqEa5qWMZUeCdtgyeONp/06oexbrNbDnazDFbbu/pQPxnvvLOuq8xNMgmYwyk\nLf7SY4fIJU2qLY9MwuDUgRzZuNm9oN6snrupa+QSse6seLHmqEB8j0tZBo9PFHh84vp6uy3XZ7nm\nsFyzKdYdqi0Xxw+jP160h8P1Q7wg+uP4IWEYBdyhlEgZrTKZmsDQBYYeBcxGJ+A29OhrTQiCMMTt\nBOheEHZ//oPgOQqg3av/vh90xhGy1nTxgugcdHX2UyCu+ZruE1dDMiGir3Ut2pcylLGImzoJUycR\ni4J785oAbCSb4Nz8+gHB1X9Ddv4RgSSmaUgk2XiMI4PpqDGMjNIfVhsujhcgEKQ7dfzzqRi1tkfM\n0Hh5qoyUMDnoMzmY3tYeDF4nHSMMwQ9Dou2Nvafh3HzyKWlq/I9fvJ8vPRatrT63tkouEUOIqF/D\nasMlbuo8ebRf7aXYQWu3WI26KmNF3cALqRi6pnGkkMQLQiYHUirNrkf0dCAOPAl8vfP114EngFsG\n4pNDafoSOuX27YoBfUAHLFMQ06LNnEPZBCNZi/lKm7YX4Icho9kEuq4xmo3z4FiOjx4psFRxqDse\n2USMjx0pRNVQyi0sQ+PYcFS54/MPjPDOYhXHDwkCiWVojOQsLq+2SFkG941kuye/vtvc0Y7m4p12\n6tHXe5l3mzTF0WyMY8MZjhSS5FMWk4NpPnVykFzCjMpFWsZtyxNeK2ZoHO5PUqw7HOnhWuvK5iRj\nBkcGDFUuD/jSR8b4k/OrN31OEAX2GUvDDyFrGUgBAymLRw/34QbRDcrD43mWq1G9/z/z4AhnOvX5\nl+tOp854nLipc+ZwHy0vwPGCbpWpW9VRvpeialUa6bjR06kpzk022o73xfjJz5zgi51VIYhWYa+U\nozb0V5uc2Z3rnK715k3IXnDfSIbi5eoNj+tAf9rkI4cKndXwBC03IGFGzZVUEN47ej0QzwOXOl9X\ngVPXPimE+DLwZYBDhw4xkk3w5U8d46WpMoWkyXAuwWg+RqPtM7XSJBRwciRN2jRJxQ2WGw6zpTbD\nGYsnJvsZKyTJJkzabsD7xTrFmh3lGItoBur4SLTZi0PXD7IvFePHP3X0usdGcnFGcnGklMytReWg\nxvuS5JMWxfoH7Z/XM5iJ89kHhvADuecD8S9/6gj/+jtTAPSb8COfnORTJ4eIGToxQ+foYBohony5\nREwn3llPv5vc+ePDGY6r2tOKAsDp8QI//fkT/PoLl6i0A/qSMZ480seDhwq03YDVhkvTDZgoJDg8\nkGY0H8fUo2pPQShpdtqlrzad7rnqak782IdW8TRNkO7cOJ8cydD2gh3JUbYMfU+cAwpJi5YGdude\n5ud/4CTP3D/KeCF53b6EvlSsO+mTtgyulKK8/WtL5yrb7xf+0qN8/7/4Flf3237qeIG/+9mTTJWa\nhFJy5nCB8UJS5XH3MHFtvmCvEUL8BLAipfxNIcSXgDEp5b+62WsHBgbkxMTEto7vw7xAognUMt9d\nmJ6e5l6/f5KooZGuRc1+lK2xFe+dem92znZ89pR758Pvn5R0Zrk11KVod7vbz14oo/0nKjjfWa+8\n8oqUUq77JvT6jPjzwI8Dvwl8Dvj3t3rhxMQEZ8+e3aZh3WhqtcmlYgOAx49EbZ+VjTtz5sw9f//O\nzVUo1hx0XfDUsQF1EtsiW/HevTFbYaWu3pudsB2fPeXe+fD79/J0mWrLwzI1njo2sKur9ex3d/PZ\ns72A5y+VCELJSC5+R817lK0lhHh1I6/r6auZlPJVwBZCPAuEUsqXdnpMt+JfU4fVVzVZdyWvs5M3\n7Gy2U3aPaLOcem92A9sL6OWV1P3O6+Tb+6F6D/ciKemeI9V73Bt6fUac9UoW7hZHOjuYLUPrlgRT\ndpdTB7LMllvkkzGVF7nLPDCaY25NvTc77WKxzg/+8vf44TPj/M8/eGr9H1B2nYfGcixWbYYylpoN\n34MSMZ2HDuaotr0byh0ru1PPB+K9wtA1jg6md3oYym3Ezb2xOWsvSsTUe7MbfOXVeVpuwL9/bpqf\n/bP3dTdEK70jEzd7ugqMsr6hbHzTDf6U7dPTqSmKoijK9nnhcqn79bm5G0uqKYqiKHdGBeKKoijK\nuqSUXCg2+PwDwwCcX6zt8IgURVF6nwrEFUVRlHWttTzqts/HjhToS5q8u6QCcUVRlM1SgbiiKIqy\nrulS1OJ+oj/F4f4UM+XWDo9IURSl96lAXFEURVnXbCfwPtyf5FAhyWy5vcMjUhRF6X0qEFcURVHW\nVaw5AAzn4hwqJJmvtFVPBEVRlE1SgbiiKIqyrpWGg2VoZCyD8UKCIJQsVu2dHpaiKEpP29ZAXAgx\nIYRYFkJ8WwjxJ53HfloI8V0hxK8LIczNPqYoiqJsvWLNZrDTBGa8L2oUMrum8sQVRVE2YydmxL8m\npfy0lPILQohB4Bkp5VPAOeCHNvPYDvwuiqIo+8JKw2EoE3UFHs5FzUKWa2pGXFEUZTN2IhB/Rgjx\nrBDi7wEfBb7defzrwBObfOw6QogvCyHOCiHOrqysbPXvoSiKsm+s1B0GO4H4SPZqIO7s5JAURVF6\n3nYH4ovACeAZ4HPAGeBqMdoq0AfkN/HYdaSUvyqlPCOlPDM4OLjlv4yiKMp+cW0gnrIMMpbB/8/e\nfQdJdtwHnv/mM+VtV/uZ7umxGAzAgeHAkiApkpLuVtKeDKmVtDLUkaJ0sXti3EYodE63utvd2FXE\naXWnWx1D3GOcFCJFibviyVBcUSSXIEEABDDwwBiM62lvy5vn8/541Y0ZYEx3V/d01Ux+AhOoel1V\nndXP/V6+X/5yQeWIK4qidOSWBuJSSltK2ZBSesBXgfNApv3jDFBu/9vqMkVRFGWbOV5AqekymI6t\nLxvMRFVqiqIoSodu9WDN9BVP30cYiH+w/fyjwPeBFzpYpiiKomyz1UaYgrLWIw4wnI2xoAJxRVGU\njtzq1JQnhBAvCiGeAeaklM8B3xVCfA+4H/hLKeXSVpfd4u+iKIpyRxhKx3juf/wIP3J85O1lmRiL\nKjVFURSlI8at/GVSyq8BX3vHst8Bfme7limKoijbS9MEQ5nYVcuGMzGWajZBINE0sUstUxRF6W1q\nQh9FURRl04YyMbxAstpwdrspiqIoPUsF4oqiKMqmDWVULXFFUZROqUBcURRF2bTh9qQ+qoShoijK\n1m05R7w9s+WvABNXfo6U8r/uvFmKoihKN1ub1EdVTlEURdm6TgZr/hXwFOGslv72NEdRFEXpBQPp\nKLomVGqKoihKBzoJxBNSyt/ctpYoiqIoPUPXBIPpKPMqNUVRFGXLOskR/6oQ4h9sW0sURVGUnjKc\njakccUVRlA50Eoh/hjAYt4QQVSFETQhR3a6GKYqiKN1tJBtjvtLa7WYoiqL0rC2npkgp0zd/lXI9\nNcvlrcUaqajJkaEUQqgJMZTrc7yA0/NVhIC7RzKYuip4tCYIJGcWarRcn7tH0iQit3SesjvacCbO\nk2eXkVKqY1gXazk+pxeqRHSNYyMZNQGTomyzIJCcXqhiewFHhzd3Htry2VyEfl4I8Vvt52NCiIe3\n+nndyA/kjn32xeUGpYbLdLFJpeXu2O+53QWBRMqdW0/dYrbcYrlms1S1mS/3firAdu5bqw2HuXKL\nUsPh0kpj2z5XubmRbIym41Ozvd1uinIDl4sNVmo2CxWLlbq9281Rtsmdcv7rBSuN8NxcrDtcXm1u\n6r2ddKv938BjwM+1n9eBP+jg87rKucUa3z6zxKvT5R35/HwiAkDU1FQP3hYt12yefGuJp8+vYnu3\nd+GebNxE00DTwse97JXpMt8+s8T5pdq2fF46ZmDoYQ/f2n6l3BpDqpZ4T5grtXhtpsJizSIVU+eb\n28FSzeLJt5Z45sIqjhfsdnPueOmouX4eyiU2d47uZI98REr5oBDiZQApZUkIcducBddq4y7XbPxA\nom/zrbzxQoL+dISIrmGoNIMtWapZBAFYgU+l5TKY1ne7STumLxnh8YP9CAFRo3e/p+cHrNTCHrmF\nis2hwc4z3GKmzvsO9eP5knikd/82vWikHYjPVyyODKlsxW4VSDg2kiEeUR0/t4ulqk0QhGlHlZbL\nQDq62026o8UjWz8PdRIBukIIHZCwPsHPbXNZNlFIEjU19hUS2x6Er0lEDBWEd2BvPkEiotOXitB3\nB/SExky9p4NwAEMP96moqTHRn9i2zzV1TQXhu2B9Uh81YLOrTfQnSMeNbbnwVbrD2JXnv+Ttf/7r\nBVs9D3Vyafz7wP8HDAoh/hXwMeB/7uDzuspYX4Kxvu0LFJTtl42bPH6of7eboWzS4aE0h1Xv6W1h\nKPN2j7jSvQ4NplUQfpvJJtT573bRSdWULwohXgQ+Agjgx6WUp7etZYqiKEpXixga/amoml1TURRl\nizqpmvJ5ICal/AMp5b+TUp4WQvz29jVNURRF6XZhLXEViCuKomxFJwnKPwz8kRDiF69Y9g87bI+i\nKIrSQ9TsmoqiKFvXSSC+BHwA+LgQ4g+EEAZhioqiKIpyhxjNxpgptVQ9Y0VRlC3oJBAXUsqqlPLH\ngGXgO0B2e5qlKIqi9IJ9hSR126PYcHa7KYqiKD2nk0D8r9ceSCl/G/jXwGSH7VEURVF6yFoZysvF\nzc0mpyiKonQQiEsp/7kQYkgI8aNCiB8FnpdSfvhG7xFCPCKEeEYI8ZQQ4vfayypCiCfb//ray/5x\n+3VfFUJkNrNMURRFuXXG+5IAXF5t7HJLFEVRek8nVVN+Gnge+Djw08BzQoiP3eRtl4EPSymfIKw/\n/h7gdSnlh9r/ikIIE/g1wvzzPwF+daPLtvpdFEVRlK0Z64sjBEyuqB5xRVGUzeokNeV/Ah6SUv6S\nlPIXgYeB37rRG6SUC1LKteH1HuADd7d7yP+NEEIARwiDcw/4JvDoJpYpiqIot1DU0BnNxplSqSmK\noiib1kkgrkkpl654vrrRzxNCHAf6pZSngMOEvdp54MeAHFBtv7TSXr7RZe/8PZ8WQpwUQpxcXl7e\nxFdTFEVRNmpfIcGlFZWaoiiKslmdBOJ/J4T4uhDiE0KITwB/C3ztZm9q54H/O+CTAFLKogzrXv0l\ncC9QBtbyvTPt5xtddhUp5eeklCeklCcGBga29CUVRVGUGzs4kOLCcl2VMFQURdmkTgZr/gbwh8Bx\n4D7gc1LK37zRe9q1xr8A/IaUckEIkRRC6O0fvw+4ALwF3Nte/lHg+5tYpiiKotxiR0fS1CyP2XJr\nt5uiKIrSU4ytvKkd/H5dSvlR4CubeOvHgYeA3wnTwfkfgD8QQjSAi8A/l1L6Qoh/DzwFlICfk1K6\nG1m2le+iKIqidObocHhz8vR8jb35xC63RlEUpXdsKRBvB8tNIURWSlnZxPu+BHzpHYsfvMbr/oSw\nEsqmlymKoii31l3DaQDOzFf5wWNDu9waRVGU3rGlQLzNAl4XQnwDWB+lI6X89Y5bdRvx/IDLxSYR\nXWOsT/UU3QquH3B5tUkiojOai+92c5QbaDk+M6UmuUSEgXR0t5ujbFEqarCvkODUfPXmL1Z2zULF\nom67jPcliRidDBFTuoFan7eHTgLxv23/62lVy2W21GIwHaWQ2v5AYHK1sV5fNx7R6d/m3+F4Aafb\nJ7+7RzJ33M44XWyyULVIRcNNeW8+zkypxWwpzFVNRgyyCXM3m6jcwKn5CqWGy1uLNcb6EgxnYpRb\nLtWWy+GhNNm4Wne94r69Ob5/cRUpJe3UQ6WL1CyXk5NFlmoWhrbKvXuzHBvJEDP1m79Z6TpPvUhV\nSgAAIABJREFUnlniu+eWOTKUxnID7t2T3e0mKVu06UBcCDEupZySUv7xTjToVntjtkLT9pmvtPjQ\nkUFqtsdyzWYkGyMZ7eQ6JWRobwfGprb9QfJ8pcVyzQYgG28x0Z/s6PP8QBJIianvbkBfs1xsL3jX\nhUvT8ZgrWwykomTiBmcXqjhuwLMrDY6NZCg3XQbSEQCEAF3fnYCg6XjUbY+BVFQFJW2eHzBdahE3\ndYazMeDt/WO23EITggvLdWKGjqlrXFyu88D4u6qSXmWlbhM1NNIxFbDvtof29/HXr84xVWyyr9DZ\ncUjZfgLBK9NlSk2HqKExmImSihocGUpf8/VVy8X1gh3poFI2Z6Fi0XJ9hjNRooZOxXL5/sVVlmo2\nthfc9Di5RkrJSt0hHtHXO6+U3beVNfGXtPO6hRB/IaX8qe1t0s56Z29N1NBo2j4RXQckL02V8H3J\nUs3i8YP9Hf++fYUE8YhORNd2pGc2GzdZi+9zHX5+y/F5frKIHwQc35vb9t77japZLs9fKiIlHB5K\nXXVSf3W6TMP2mS41+eDhARarNnNXVGqImhoHB1KkoiZxc3cONpbr89zFIn4gGetLrOfP3ukurjSY\nWg3vDsVMjVwiwj2jGZZqNjFT46WpMq4fcKA/halr5BORG37e5EqD80t1hICH9/epYHyXPTzRB8Dz\nl4oqEO9CU8UGFctlpW4zmo+jC0HuOnecqpbLC+1j8JGhNOMFlVa5W8pNhzdmK5SbDk3HY6I/xXtG\nM+QSJr6U7O9PcngwtaHPurjS4NJyA02DRw8USERUMN4NtrIWruzeO7BdDbkVVus2r81UiBgaJyby\nRA2d43tzFBsO2biJEAJdCHwklabLi5dL7MnF13vvtkIIQdTQqFkembiJrm1v72guEeF9h8ILhqjR\n2S3GcsvB9QIAVuvOrgXithewVo7YcoP15Wvrz/ECDg6meHGqiOMF3DWSJhUxuGdPlkIyghBiS+vM\ncn2Wqjb96UhHBygvkPiBXP9MJaS1L4D9QHJmoUYyYnB0JE0iomNogkrLIaJrFFImJyb6broOLC/8\n20oZpmgpu+vwYIp8wuTp8yt8/MTYbjdHucLfv7nAd84uUWk6jPclefxggSeODFw3LcV2rzgGe+oY\ntpuEENiez6szYUeF7Yb/7h/LU0hFODSY2vBd17XzURCEx8yb9HUot8hWog15ncddb7Fq4weSluNT\naboMZsJb4EOZt4O2ExN5VusOp+erlBoO1ZbbUSDedDxevFxCSqhZHsdGMzd/0yZ1GoCvGUhFKaQi\nuL5kb373Bjn2p6IcHkphuQEHBt7uWVus2owXElSaLjFDo9L0MHSBoQnuHs1ctR634qWpEk3b53JR\n44nDW58AKhU1uHs0Q7Xlsr/DVKHbyYH+JImIzmrdYbFqUbc8TF0w106vEghSMZNk1NzQhdCB/hQC\nQczU1O3zLqBpgo/cPcTX31zA8YI7brxKtwqk5I3ZCtWWS9PxGcnGeORA4Ya54QPpKIcGU9heoI5h\nuywbN5noT7Jat3l1psKFlTq2H3B0OMNQNrap1MdDgyl0TZCMGORUFN41thKI3yeEqBL2jMfbj2k/\nl1LK7Y80t8mefJxS0yFmauST794IPT9gphTmqhZSEUoNl0x8838izw8oNhwycZOG7WG7PhFDJ+jy\nWecMXdtwrtlOu9at7bX1N5CKMpiO8uZ8lXTM4P2H+snEIyxULFYbNvsKyRumpFQtl4iuvetEtLZ6\ngm1YTXtycfaoii1X0TTBaC6OoQnOLdVIRg3iEZ3ZUgs/kBwYTDLRl+TI0I1vs3p+QMP2SccMlfbT\nZX74nmH+44szPHtxlQ8eUbMZdwNNCIayUd6cr3DPaIb7x3Ms1SxqlnvDFKJOxxsp2+fQYIqa5VF3\nfJCSmVKLbMzcdMde1NDXa/4r3WPTUaaUsmeHWGfj5noaxztVmi4vXF6lYfkkowZHh9McHgpTHjbr\n9dkKq3UH2/OJGBqWFzCSi6ugoUNr66/ScpkrN7Ecn5ipc26pzr17srw5V0FKaDo+D7XzVd9parXJ\nW4s1dF3w6P4C8cjbm/P9YzkWq5Yqo7fDzi7UKDYcILz4SUYNWq7P4wf6b3ryl1LywmSJhu0xmIly\nfG/uVjRZ2aAnDveTS5j8+QtTKhDvIqamkY4alJsuphDMFMNxNemYSd81OqWU7hI1dB49UCCiC772\n+gINx+PVmTLJmMGPHh9Vd596nFp7ba/NlinWXS6uhCXRk1GDTMxEuyKnOwgk08XmehCxptgIB1Gs\nWctrrtkeQSDJtA92u12J5Hbg+QHfOr3I6fkal4tNNAFLVZsgeLvSS/wGt1yrlguA70ta78jfTkYN\nDgyk1KC/HXZppcF8pcUbsxVszycTMxlKx9A1Qbnp3PC9gWR9X6tb3g1fq9x6MVPnH50Y4+tvLqrp\n7rvITLlFueWyULGotMJjoKaFxQqU3mC5PvMVCz+QVFseEUNjqWZRrNtMrTap2+p42KvUkNm2iK7R\nl4yQjRs8fKCPzDWCsTMLVV6frZKM6Hzo6CCpqMHF5ToXlxvomuDRA2EP6z2jaWZKFsf2pCk3PCRS\npSlskzMLtfbJxGE4G6diuUR1jZenyzy8v4+a5VG4QQ/PwYEUgZQkIrrqCdol+weSvDFXxhAaq3Wb\nExN56rbH2YUaUoazNF5v8itdE9w9ElZaGVcTZHWlX3x8gv/36Ul+9+/P8m9/+v7dbo4C3DWU4pun\nFsnEDCwv4LGDBaKmvi0lepVbYy23e38hwXAuiuUEJEyD//TGAmN9CYwVwQcOD1zVeaj0hjt2L3T9\ngJYb9sZdWmlgez6JqM6eXIrkddJRzi81uLhcp257HB5McddIhqYT9qr6gWS+0uSN2SqaEDy4L89o\nNs7oDtfYDwLJ+eU6ni85PJS67XvdzyxUqTRtarbHsZEs55frBIHktZkKnudzfCx/wwNRPKJ3nM5w\ncbnOQtViopBUM3duQhBIzi5UeeFSkeVamOv//GQRTYQDLtdy9N95pwKg1HA4s1AjHTO4ZzSj/u5d\nbE8uzief2M9nn7zAx967d1vKwCqdWajY1FoelabD9y+uMpiJXjd9T+k+S1WLuXKLSyt1zizU6E9H\nGc8nkMDrMxXKLYd0zOSxAwWiWnhH2PZ8DE3b9kptyva7IwNxP5A8f6lIy/FJRnXmyi2SUYO3lsLJ\nfWqWtz5LVcvxESK85Tqai/HmXIWYGfbAZhNmu3QQLFQtvvLSLPMViz35OIOZ2HqwcGahymypxd78\n9teUXqxZ67WZI4bGoQ3UE3W8ANcPeq43ZKrY5Mx8jbOLNRq2h65pBIFESsnphRpTxSaPrzT5uYfH\nEULsSN6cH0guLofpSxeW6yog3ISlms33Lxa5sNyg0nJpWA5nF6v87WvzHBlK8RMP7mU0m2DiHQPI\ngkByar5Cw/Zp2B5jfQk142aX+6c/cIivv7nAr3/pZf7qn75f3RHcZa/OlMNJxiyX751bYqFdtWgw\nE2OxarGvkODQoBrD1I1myy2+cWqBN2aqrDQsbDdgteGADFNUUjGdluMTNTT++tU5HhjPkY6ZvDFb\nwdQ1Ht7fp2ZP7XK9FYltE9cPwhKGLYe3Fm0MXdB0fFIRHccLeGmqyHMXVwHCdJWESX8qStPxySdM\nWq7Hq9NlXp4q8UP3DPOBIwM8c36F2VIL2wvQEIz1vX3imSu3kDL8/0YDcbtdu/VmpQkTpoEQ4aC3\njUxeY7k+z10q4nrBDVMAupGhhd/TdgNmSy2mVprcPZLER6fp+JxeqFGzXZq2x8HBFA/uyzOY7qyk\n4TvpmqAvFaFYd3pqUKfl+mg7dHGyUTFTY7Vhc2q2zErdDi9wDQ2haVxaaeL7krtH0kwXW/hSsq8v\ngaYJXpkpM11sUWm5vGdPlmSkN08qTccjauh3RA9VMmrwhz//Xn7ys8/wM597lj/79GMqGN9FB/sT\n/N3rLk4AU0ULU9c5ebnE8T1ZDF1jtmzdtoG46wf4gezZYLRuubw5U+Hp8ytULZdUxGCsL86Z+YC6\n4zNRSLK/P4nlBiRMndmyxUBKtgsXeCzVLMb7VAWcbnbHBeLTxSZTxWZ4KxyTpuOzUrOwXI89uQTL\ndYuVusPF5QYJU2MkG+eePVlOz9eotBy+f3EFX4LnBbgBfO31eYYyUfqSEfb1J8nEDH7qvWNX9diN\n9yWZKTXZm99Y0FtuOrx4uchcxeLwQIoH9uWvG5BnEyaPHijgBXJDvYRNx1+ftKfScumlaTdGcwl+\n5PgIz5xfotR08QPJpdUWR4ay5OIGTcdjuebw9PkVVuoOuURk2wNxgAfGcjh+sG3123faUs3i9ZkK\nmiZ4eKJv1+6EmLpGKqLj+gFNJ0DXBUIEaAH4QcDX3phH8va097oQ63Xj+1NRCqkojx4o9GQO5NpY\nknhE55H9fRi3eQoZwOGhNF/45CP8/Oef42c+9yxf+pVHN3wMVLZXtRUG4QAB0PR8NAGFVJSG4922\n4y2u7Hi6ezTTkxeDQ+kYF1bqrNRsPAme7xCsBqRiEQ4MphjNxfnx+0cptTwWqxbjfQkG0lFKTYfz\niy10UUPXtJ787neKOy4QP7dUo9J0CaTk4Yk+6pbHbFkyuVDjtekKDdujkI5iOT6aplO3XWbLTaKG\nztRKA18KYoaGgyCmCTJxk/5UBG0ky558gqPD6asC4prlrk+OsBFBIDk1V2VypUmp6aILQV8qyuHB\n1DWDvyCQzFcsANJR46ZBSj5hMl5IULe9npyoQSDxEQRSogmB5QUMpAxszyMbN/GDgIbtMV1qcnKy\nyHhfYkvpI44XULc9cvGrK+cUGw6vzZSJGjrv3ZfvibJR5aaLlGGlmJrl7VogbgjB6YUapZZLAGhS\nkolHGMnGqbQ8Wo7PN08t8p49GZJRk6WaxUguxt0jGWbLTfbkEjsShJ9fCtOaRnPxHauxW2qGlSpa\njo/lBaTugEAc4L6xHF/45CP8wuef42c+932+9CuP9tRduNvF0xdWr3oe1wV+OzD/0F2DQNgBVLM8\nRrKxrrtQnC42ObdUoy8Z5b692Q1PYtOwvfWOp1LD6clgdKlmUWq6eDKcQdHzw2o3QkAhEeH43gxl\ny6M/FWE0G+PsYp3J1QZ7sjFaTvh9O/nutuczV7bIJ0w1CdAOueMC8UrT4425CqfmqvzFSzMMpWME\nQcBcxaLcdMjGDUxd5+hwmrrtMV1uIQlTVB471E9yuoLrB3zk2CDFusPBwRTD2QTD7UGZrh/QtD0M\nXaNmubw8VQbg+N4sgxuY+fHiSp2q5a7nfPUlI3h+wFPnVnC8gIn+5FVB/Wy5xWS75GLU0G56khNC\ncGSod29BrjQcbMdHyHDGOEMTvDFXpe74ZGIG/akY431xzi7WefbiKpWWyw/cNchjBwsIIdannr9R\nekAQSF6YDMcQDGVivGfv2yNuFyoWni/xfI9y09nQOr0R2/ORkh29bTreF154RXRtV9NpTi9UmS41\nsZwASTjNsu0ElJs2I/kkuoDVhsNi3UFrT4h1er7K8b05IobG5GoD1w+uu42v1m0atkc+ESG9iRzy\nmVKLIIDZUmvHAvGDA0nOy/Cu1UZSyG4n943l+OKnHm33jIfB+HhBBeO3SiAlcfPqwHqp5nBmvsqB\ngQQP7e+j5fi8eLlEEEgurzZAQEzXODCY7orqUnPlcB9dqdnYXrDh42VfMsJoLk7L9Xp2gqJiw6Fu\nuevTmEvCC/t79yTY2xfn22dXSMcM+hKR9RmGU+25GUZzcRqO29HF75uzFS60K8Ot1SwPAknT9UlG\n9E3N7Klc2511RgAGM1HGWgmePLOE5fpcWKrTn4qit3u3bS+gbru8tVil4fi4fkAubjKQjpGOGfzE\ng3s4ebnId99aIRHRcHzJnnw4eGym1OSZ86ss1cLBL33JKH4g0TWxXl0FwmD95GSJqWKDe0ez9Kej\nBFIyko0DAkPTODqS4Z7RDKWWw+WVJqcXqhweTLFSt68KxKNXHGDvhJqw5xdrLNVauO2jUrHhEDN0\n/EASSDgwkOS943kcTzJXaTFVDP92h4ZSxEydFy+XkIFkXyHJSC52zanU5ystzi7U6E9H3lWbdSQb\nY7luEzOuPTvrTKnJat1hXyFx096Duu3xwqUigZS8Z292R9JoIAzyH+yCGVNPXioyudJgbU/wgYbj\n4wOPHkhiOS5RQ+I4PlXb45Sort9dOjlZxPJ8inWH4WzsqupAUkourjR4bbrMdKlFPhFO/HRgYGN3\nofbmE0wXm+zJv7vHaLlmM1tuMZKNMdTBRVcuEeHEHVyl4j17s3zxU2Gayk//4bN84VOPbPguodIZ\nTQiK7Tsya7x21bBs3OT8Up0gCJgpNZkptZhvj2nak4/zIV/y+KF+Vuo2MVMnGdFZrtkkosYtvaDc\n25fgrcUayYjO2YUqqZjJwQ3s30KITc8+2W0s1wcEGmFaUQDYnuT8UoNSy6U/ESETN0nvyxM3tTC9\nTxfsySeImTonJ4u8dLnEA+O5LfVoL9cdLq+Gc3as1G1Gc3Feni5Tajj0p6PcP6YmVevUHReIHxvJ\nMF1s0HJ8lus2yDAwziWiDKRM0nGD1bpL3fZwvIBDg2mO783i+ZKvv7FAAMyVWlRbLpouGMs1WW1Y\nvHdfH7WWx0rdpm55nJwskYkb9KeiPHKgwJ7c2yfx+bLFazNlapZHseEwmouTjBjhNN/9SWKmRtTQ\nGUhHqVoepq7Rn4xi6hoHBq6+qh9MxzgxEQYlt/ttIz+QfP31OVpXxMZBAE3XQwhB3fI4u1BnIB3j\n6EiaWHtQX6XpEgSSYt3B9yWXiw3OLNTQNcHjBwsMZWIMpKMIIXD9gDMLNVJRg5rl8cHDbx/EFyoW\nEnndGQMdL+DMfA0IS/A9eqBww+9Ts9z1HvpK02UgFb1texeKDYfn23cZrtR0A3QB37+0guMGCE3w\n4aMDPDTRh+35DGaiYYBQbFFqORwbyTBbapFPhIOoAS4sN3hztsKZhSogEMAzF1bxA0k6ZjKQjt7w\nDsihwdR1g8JT81VcL6DYsDsKxK9HSnnbrvN3undPlj/79KP8/P/zPP/oD5/l8594SJ3EbwE/kKzU\n7KuW6bogbmq8dLnEUtVmue7gej4RXWOhamHqWhjcxQwuLNe5tNxA06CQjLBcc9A1EdYiN7T17TcI\nJKcXqrQcn6MjmZsG6k3Ho2Z5DKSiN00525OLsycX5/WZCotVi+WaQyEZ6fic1wv73/6BsDJbcMUy\nT8Jq06Xh+NSaLoeH00gfxvoStFyfxYqFlGFKiueH55jVhrOlv9eB/iTFukMy+vZA80ornHjtZhOw\nKRtzxwXiiaiO7UsMXRAEYa9cuekR+B6+HyMIJAs1G02ALkATMFFIcG65RsP28XxJteXSsD1MUyMd\n0/na6w3OL9bJJkwSEYOAgHLTwQ8ktZbPnlyCmWJYIvHB8RxVy2G62GC61OLu4TT72uXapARNE1cN\naJroT+AFYUrKwYHk+kHjwnKdSsvl0GDqtg/A13zj1DyvzNWvWibaA2d1Xcd2feYrLZ67uIKmCRK6\nznh/Esvz+ftTizx2IE/DcpktNqm104fKTYcH9+WZKIQpP7oQRI1wsp+BdHQ92JuvtHhztgqE6+md\neee25/PCZJGzizVGc3GGMlFW6japqHHd26iD6RirWQcvkKRjBk++tYypaZyYyPfsCP/rsVyPuuPh\n+vKq5ZLwBLNYbuELgSbDnvN0zOTERAFDE/zJs5NUmy6GLvibV+f46mtzHB5M86kP7MfzJecWawgB\nhwbTJCJhOdJEROdrr89zbDTDYDrG3nw87NG7IjhYqFicmq+Qjpk8OJ6/ZrCeihqUPIdUdHvLJUop\neWmqTLnpcGSot6oXdeLocIYv/+qj/MLnn+djn32GX37fBL/w6IRKVdlBEmi6V+93lhNQbLks1512\nh4WgkIqyVLO4ZzTDYtVCE5JXpkq4gWQ0F6dSd3nq3DKpiEkhZfDCZJGxfJyPHhtud1y4zJfD8UqT\nK431EsDX4ngBz10q4vuS4Wzsuq91vICq5ZJPRHhrscbrs2U8XzJeSHR8jFzb/xMRgxP78l2XF78m\nHdVZqV074PX8cCxTw/ZJxHQ8P+CL35+i6Xi8PFXm5x/bRz5p4gcwkomt36HfjIMDKSJGWI98rTPi\n6HCGuXJLDb7eJlsKxIUQOvBvpJS/sc3t2VEN2+MvX57h26cXWahY67fIA6BsQ8W2kO94z2zZ4uTk\nKiXLw3Z8dA1WGy5eIEmaGqdmfVxfYjk+Q5ko+/pTLFRaeD4sVm2OjWZ4c67MUCYM3J6+sMJMsUXE\n0Dk0kGK8kGIwE2WoHSxUWi6aYH2a9aihc887ZgWqWS6X2rWsz1PvirSDW+GbpxbetcwFii2fiOaT\ni5uU6uGtVV8S9uYUmwymIrxwqciXX5hmIBWhPx3l0nKDvlQUGUguLoU9PjOlFsf3Znlof56a5dF3\nxQVO0wlHpEdMjUC+cysJB0RaTsDBgSR9yQhSwitTZaKmxuMH+6958NM1sX4Cemuxhu9LfN+n1HTa\naUq3j0zMZDAVwXv3n46WE6AJ8GW4L56aCwdPLpSbpONRLq00WKxaJCI6ixWLlhuwWLV4aCKPE8j1\nOv65uEmxYbNUs4hogpipIxC8MVvhW6cXiZoaP3bfKFFDZzAdZb4S5p1Wmi51y1u/6LrS/WM5apa7\n7bfhW65PqRGeXOcr1h0TiAMcGEjxtV9/gn/xt6f4/Pcu8e+fusRwJsaefJx0zMDzJa4f4AXh/xMR\nnX19SR7cl+OxA/2M9cW7vhezm0gp33Ve8yRUGw4XqWN7HhFdZ6HSwvYkmbhO3Qo4u1jnzEKdh/f3\nsScf56XpMlMrdUCQiOggwnEZluuTiUfaKWMC15dX5ZVLKbm00qBuu4xkE+gafOPUIjOlsJyv4wdc\nz8nJIk3Hpy8VodRwGEzHsL3wbmPHgXjVIgigbnnhwPwu7dD6jydnuN5fyJftNCMnHITfsD2mig2q\nLQ9daJxbrPP+w/0YmuCFyRJNx+Pukc1NiKZpYr2zcM1oLq7m0NhGWzq7SCl9IcR7hRBCymtEJbeQ\nEOL3gBPAS1LKz9zotQvlFp/9z2eZqXrX/Pm1vojtS16brWEIiEU0Wk6wnp/cdAMCwmoUluvjB5KW\n47NSs9E1wXA2hi8lddsn3nLZm49juwH5pMliRScVN0nHdJYqFmfmq/Snokyu1JkutvjI3UN8+O6h\na7YzZupETQ3bDcjE7pyJTRYq9nV/5gSw1Lg6D7Lc8ojoDi3Ho1i3abkSQ4MjQyncADwZUGm6LFQt\nCqkI44UEM6Umx/fm6G8PevG8gL96dZaXpsq4vmQgHeHeds5heNBrkokZDKSj7TEGPkeG0pxdCFNU\nHC/YUC/ESDbGSs3G0DUKyZsPqFxLgdqTj3fdbKqeHzBfsUhFjfU8el0TnFuoXPP1PuEJZY0rodTy\n+fOTcyQi4aAxU9fRdQ3XD7B9yXLd4dtnlsnEdZZqNi9NlYjoGnNlm0zc4N49WX76xF50TfC114pM\nlVqMZKMgJXtzCe7Zm2M0G+fCcp1CMko6du1Doa6JHTlBx02d4WyMYsPputJx5aZDqekymovtWInO\nbMLkf//4fXzmI4f51ulFXm2nHBQbDoYmMHSNmKmRihrUbY+/P7XAn5+cBmA4E+PERJ7je7OMZOPk\nExEMXWDqAl3T1gPP8MwU1lNeey5lOJZEEi5cX37F6xwvwHJ9LNfHbj9e+78AUjGDdCwcdJuOhbnS\nV6ZWrD268mJh7VH4++RV7blR2wL5drsCubY8XBa84/1OOyBrOeEgvR+6ZxgIU0beSQLTpRYzpRan\nZiukYhrlZjiIWhfh2CMpodpySEV0GpbLK1MlZkpNZCAZyITja6KGzmszZeLtx8dG0jw4kb8qSJsu\nNvnm6XnemK1zdDhNPKKzWnewPJ+EqXP3cOZdlYsatsfp+SrnlmqMZuO0HJ+xvgSz5RZHBzIEUjK1\n2qQ/HbnmOJ+N2JuPr19kd8t5NOzwselPRdY7416+vHzd10ug5YcXFSs1i6fOLrNcs9E1jb6EweXV\nBvmEyb5CkkZ7vNNSzVZBdJfppJvnZeCvhBD/AWisLZRSfqXjVm2QEOJBICmlfEII8VkhxENSyheu\n9/o/+s71g/AbCQBHgmNffV1qamH+nSslfiDRNMFsucFSzSIdMykkoyRMjeW6Q81ySUQN3jueo+UF\nPLy/QDpm8NLlEt84tcTkagOBJJCSdCzCsxdWeexgP/FrTF5i6hqPHihguf76znonuLRU2/R7lupX\n39JzAjizUCce1YnqGkOZKJansVi1aNo+94/lqNs+7zsUTst9brnOuaU6s6Umhi7YX0hgueF2cHq+\nyqWVBtPFJg/uy/P4wf71cobHRjNcXm3Sn4puqMRhOmby+KGNTQXesD1eaVfjaTjeu+6Y7LYzCzUW\nKhZCsD69ueMHnF5sbepzJNBwoNEeohTXwQsgGdWotVz+5rVZCqkIlabLasNBiLD2uKELLi7X+crL\nszQdn5bjYbkey3WBO1vl1ZkqU+UWR4fT5OKRMIXM8rD9MNCq22HFopudrBwvYLVhk09ENt07J4S4\n4a37NUs1C8sJ2JuP35L66eGEZiWCAEpNZ8fvto31JfjE+/bf9HVSSs4vhZWQXpgscXKyyFdfm9/R\ntr3TWly9u11PG/ORo4Prgbjj+dd8zfrdKQl28+1zWyDBbRcdb7gBL1xe5Y25CpWmx9rZs9y0iBhx\nIjpMrjZZKLfIJExemS7z9IUVfuUDB7lnNMv5pTp//Mwlnru4iutLKk2H+8eyOF5AX8LkntEMb85V\neHWmzFgusV656NJKg3IzDJITUYN7RjPkEpH1il9Pn1+h5fhMl/T1Y/X1NGyPquUymI5d1SHSn4ry\nxOFrj/fZSTXLZaXuMJyJvev8/sp0mabtM1XU+MDh8Hs9d6F4889sefzdGws0rDDvfk8++qwjAAAg\nAElEQVQ+jg8s122eu1QM59TIRKlbt2/N+F7WSSDeB6wCH75imQRuWSAOPAZ8s/34m8CjwHUD8S+/\nsrgtv1QA+Xg4mDJMUwkQCExNo9JyMQ0DUw+ratRtn6V2znk+0eTocPqq6gnxiE46ptN0PPYVElhO\nQCZusn8gialf/8Rr6lrX9YTuNOMGf4/N8MPuJXRTI580QWo0NZ+pYpNUzMCXrE+6UkhFcLyAqKEx\nmI7xwL7c+uyoUUMPB+0Kge0G1Cx3vXxUOmZuKNDaCiFYn01V0N236NduijdsH1MD+/p3oW/K8kED\nWp4EXJIRg+Wag+eHqS0RQ6c/FSEXj5BLmDRtH02DuGlwcDDCSCZG3fZYrNl4vmS+bNGXDIPomVKT\n+YrFXLlFpl1iMG7q16yMs+bVmTKVpkvM1Hn/4Y1dRG1Guenw2nR4F8Hx/Vsy86EQa9uU7KotSwjB\n4aE0h4fS/OJjEwDrd7MqLRfPD3ADid8+Frf/QwjR/n/4vTRB+2eivaz9mvXHENF1YqYW3nk0NKJm\n+Dyih73ETdenZoXpTFXLo2F76+lq6zH6FcH62j4g5RV/XxFWM7lR2zRxvTaGr117He3PiBo68YhO\n3NTD1JG3/3hb/7sDjg9C+FwZzjctiUwLFqp2mE6pCxwvADz8AC4uN7hrKM2zF1eoWd7692u5AZlE\nhD5N47EDfZRbHuWmS0TXqNse97ZLxeaTERYqFv3pGO/d9+4xM2t/72ulCV7J9QNemCzi+ZLljM3x\nvbs7OHhtbIjrBSxULB47ePWA/rXj+dp6BihuYDykpoUpQYahk45JDg+m2F9IULPDOzuxiM5BVaWo\na205EJdS/vJ2NmSLcsCF9uMKcM+VPxRCfBr4NMD4+DhHC3HOLt+4V06jffsQMAlvmxs6JCIGOhKh\nCcbyCX78wT1YbsDT51epWS5jfXEOD6a4sNRkrtLirqE0P3DXIC9OlfD8ANsNGEhF3zWJzr17wrJ1\nR4fTWG7AfWM5CqkIyajRtYNHdssn33+A/+WrZ7b03rgRzthoaBqphEFc19nXn+TnHhlnttTi+UtF\nmq5PzDTYm4+v/+0H0zE+fPcQqzWbVMzgvr359V6Me0YzxEyNmXKTvkSU/C3KMUxEDB4Yz9Owva68\nxXjXcJpk+7b92m3jZFTnv3pglC+/OLehz9DhqhO/LiAXN9o94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IOiKDujk0D8l66x7BM3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13885ba8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce a scatter matrix for each pair of features in the data\n",
    "pd.scatter_matrix(data, alpha = 0.3, figsize = (12,8), diagonal = 'kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix\n",
    "* This is to cross-reference with the scatter matrix above to draw more accurate insights from the data.\n",
    "* The higher the color is on the bar, the higher the correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2agnhTsCtrXwlMH5f5F2Ab7byA6aVX9TKd5mh/lzvMSfnREqSJC1ey4GpFdZHA+eOlb+4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\nkZIkadBcnd2PSaQkSZI6M4m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      "text/plain": [
       "<matplotlib.figure.Figure at 0x138870ba128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_corr(df,size=10):\n",
    "    '''Function plots a graphical correlation matrix for each pair of columns in the dataframe.\n",
    "\n",
    "    Input:\n",
    "        df: pandas DataFrame\n",
    "        size: vertical and horizontal size of the plot'''\n",
    "\n",
    "    corr = df.corr()\n",
    "    fig, ax = plt.subplots(figsize=(size, size))\n",
    "    cax = ax.matshow(df, interpolation='nearest')\n",
    "    ax.matshow(corr)\n",
    "    fig.colorbar(cax)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns);\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns);\n",
    "\n",
    "\n",
    "plot_corr(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "Are there any pairs of features which exhibit some degree of correlation? Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? How is the data for those features distributed?\n",
    "\n",
    "Hint: Is the data normally distributed? Where do most of the data points lie?\n",
    "\n",
    "### Answer: \n",
    "I have plotted a correlation matrix to compare with the scatter matrix to ensure this answer is as accurate as possible.\n",
    "The follow pairs of features seem to have some correlation as observed from the scatter plot showing a linear trend and the correlation plot showing a high correlation between the two features. I have ranked them in order of correlation from strongest to weakest:\n",
    "\n",
    "* Grocery and Detergents_Paper.\n",
    "* Grocery and Milk.\n",
    "* Detergents_Paper and Milk (not too strong).\n",
    "\n",
    "These features that are strongly correlated does lend credence to our initial claim that Grocery may not be necessary for identifying customers' spending habits.\n",
    "\n",
    "Grocery has a high correlation with Detergents_Paper and Milk that corresponds to a relatively high R2 score when we regress Grocery on all other features.\n",
    "\n",
    "The data are not normally distributed due to the presence of many outliers.\n",
    "Evidently, most are skewed to the left where most of the data points lie.\n",
    "This indicates how normalization is required to make the data features normally distributed as clustering algorithms require them to be normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\n",
    "In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Feature Scaling\n",
    "If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most often appropriate to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a Box-Cox test, which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    "* Assign a copy of the data to log_data after applying a logarithm scaling. Use the np.log function for this.\n",
    "* Assign a copy of the sample data to log_samples after applying a logrithm scaling. Again, use np.log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:8: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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LzJZ0zm1l/cCkKeWchiiGnK+bDL2IyYKKIgq8vdLFDWLiJOXUco2rLYsoTrH8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AyTduF7g8W8FyfRZrFp842U+hbvLlG0tUTZcPH+nhR14axfUC+pIbrwZYrJk0LI+R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nuwzumtAdGIS6pIEmVLo5bXmSvfmvJSMjVOzFf2/dutJhE/LDy3XMMJRRfubjBZMnHDmL+8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\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KDfe6gdOKaaEsSRztT25JcnKuZLWkGY8P3riEXqg5rSCrzTYghdmiyXyddFTbsQO7aodr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\nXzY4J/UxsZ9bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13883363a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Scale the data using the natural logarithm\n",
    "log_data = np.log(data)\n",
    "\n",
    "# TODO: Scale the sample data using the natural logarithm\n",
    "log_samples = np.log(samples)\n",
    "\n",
    "# Produce a scatter matrix for each pair of newly-transformed features\n",
    "pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).\n",
    "\n",
    "Run the code below to see how the sample data has changed after having the natural logarithm applied to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.248504</td>\n",
       "      <td>9.724899</td>\n",
       "      <td>10.274568</td>\n",
       "      <td>6.511745</td>\n",
       "      <td>6.728629</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.156850</td>\n",
       "      <td>8.192294</td>\n",
       "      <td>7.607381</td>\n",
       "      <td>9.240190</td>\n",
       "      <td>5.749393</td>\n",
       "      <td>7.232733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.299121</td>\n",
       "      <td>9.614605</td>\n",
       "      <td>9.386308</td>\n",
       "      <td>6.495266</td>\n",
       "      <td>8.266421</td>\n",
       "      <td>8.162801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fresh      Milk    Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "0   7.248504  9.724899  10.274568  6.511745          6.728629    1.098612\n",
       "1  10.156850  8.192294   7.607381  9.240190          5.749393    7.232733\n",
       "2   7.299121  9.614605   9.386308  6.495266          8.266421    8.162801"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(log_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many \"rules of thumb\" for what constitutes an outlier in a dataset. Here, we will use Tukey's Method for identfying outliers: An outlier step is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal. The process as follows:\n",
    "\n",
    "* Assign the value of the 25th percentile for the given feature to Q1. Use np.percentile for this.\n",
    "* Assign the value of the 75th percentile for the given feature to Q3. Again, use np.percentile.\n",
    "* Assign the calculation of an outlier step for the given feature to step.\n",
    "* Optionally remove data points from the dataset by adding indices to the outliers list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Fresh':\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.389072</td>\n",
       "      <td>9.163249</td>\n",
       "      <td>9.575192</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>8.964184</td>\n",
       "      <td>5.049856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.979339</td>\n",
       "      <td>8.740657</td>\n",
       "      <td>6.086775</td>\n",
       "      <td>5.407172</td>\n",
       "      <td>6.563856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3.135494</td>\n",
       "      <td>7.869402</td>\n",
       "      <td>9.001839</td>\n",
       "      <td>4.976734</td>\n",
       "      <td>8.262043</td>\n",
       "      <td>5.379897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>5.298317</td>\n",
       "      <td>10.160530</td>\n",
       "      <td>9.894245</td>\n",
       "      <td>6.478510</td>\n",
       "      <td>9.079434</td>\n",
       "      <td>8.740337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>5.192957</td>\n",
       "      <td>8.156223</td>\n",
       "      <td>9.917982</td>\n",
       "      <td>6.865891</td>\n",
       "      <td>8.633731</td>\n",
       "      <td>6.501290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2.890372</td>\n",
       "      <td>8.923191</td>\n",
       "      <td>9.629380</td>\n",
       "      <td>7.158514</td>\n",
       "      <td>8.475746</td>\n",
       "      <td>8.759669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>5.081404</td>\n",
       "      <td>8.917311</td>\n",
       "      <td>10.117510</td>\n",
       "      <td>6.424869</td>\n",
       "      <td>9.374413</td>\n",
       "      <td>7.787382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>5.493061</td>\n",
       "      <td>9.468001</td>\n",
       "      <td>9.088399</td>\n",
       "      <td>6.683361</td>\n",
       "      <td>8.271037</td>\n",
       "      <td>5.351858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>5.808142</td>\n",
       "      <td>8.856661</td>\n",
       "      <td>9.655090</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>6.309918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>4.762174</td>\n",
       "      <td>8.742574</td>\n",
       "      <td>9.961898</td>\n",
       "      <td>5.429346</td>\n",
       "      <td>9.069007</td>\n",
       "      <td>7.013016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>5.247024</td>\n",
       "      <td>6.588926</td>\n",
       "      <td>7.606885</td>\n",
       "      <td>5.501258</td>\n",
       "      <td>5.214936</td>\n",
       "      <td>4.844187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>3.610918</td>\n",
       "      <td>7.150701</td>\n",
       "      <td>10.011086</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>8.816853</td>\n",
       "      <td>4.700480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>4.574711</td>\n",
       "      <td>8.190077</td>\n",
       "      <td>9.425452</td>\n",
       "      <td>4.584967</td>\n",
       "      <td>7.996317</td>\n",
       "      <td>4.127134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "65   4.442651   9.950323  10.732651  3.583519         10.095388    7.260523\n",
       "66   2.197225   7.335634   8.911530  5.164786          8.151333    3.295837\n",
       "81   5.389072   9.163249   9.575192  5.645447          8.964184    5.049856\n",
       "95   1.098612   7.979339   8.740657  6.086775          5.407172    6.563856\n",
       "96   3.135494   7.869402   9.001839  4.976734          8.262043    5.379897\n",
       "128  4.941642   9.087834   8.248791  4.955827          6.967909    1.098612\n",
       "171  5.298317  10.160530   9.894245  6.478510          9.079434    8.740337\n",
       "193  5.192957   8.156223   9.917982  6.865891          8.633731    6.501290\n",
       "218  2.890372   8.923191   9.629380  7.158514          8.475746    8.759669\n",
       "304  5.081404   8.917311  10.117510  6.424869          9.374413    7.787382\n",
       "305  5.493061   9.468001   9.088399  6.683361          8.271037    5.351858\n",
       "338  1.098612   5.808142   8.856661  9.655090          2.708050    6.309918\n",
       "353  4.762174   8.742574   9.961898  5.429346          9.069007    7.013016\n",
       "355  5.247024   6.588926   7.606885  5.501258          5.214936    4.844187\n",
       "357  3.610918   7.150701  10.011086  4.919981          8.816853    4.700480\n",
       "412  4.574711   8.190077   9.425452  4.584967          7.996317    4.127134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Milk':\n"
     ]
    },
    {
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       "      <th>Fresh</th>\n",
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       "      <th>86</th>\n",
       "      <td>10.039983</td>\n",
       "      <td>11.205013</td>\n",
       "      <td>10.377047</td>\n",
       "      <td>6.894670</td>\n",
       "      <td>9.906981</td>\n",
       "      <td>6.805723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>6.220590</td>\n",
       "      <td>4.718499</td>\n",
       "      <td>6.656727</td>\n",
       "      <td>6.796824</td>\n",
       "      <td>4.025352</td>\n",
       "      <td>4.882802</td>\n",
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       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>10.029503</td>\n",
       "      <td>4.897840</td>\n",
       "      <td>5.384495</td>\n",
       "      <td>8.057377</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>6.306275</td>\n",
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       "         Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "86   10.039983  11.205013  10.377047  6.894670          9.906981    6.805723\n",
       "98    6.220590   4.718499   6.656727  6.796824          4.025352    4.882802\n",
       "154   6.432940   4.007333   4.919981  4.317488          1.945910    2.079442\n",
       "356  10.029503   4.897840   5.384495  8.057377          2.197225    6.306275"
      ]
     },
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    {
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     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Grocery':\n"
     ]
    },
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
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       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612    6.882437\n",
       "154  6.432940  4.007333  4.919981  4.317488          1.945910    2.079442"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Frozen':\n"
     ]
    },
    {
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       "      <th>Frozen</th>\n",
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       "      <th>Delicassen</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8.431853</td>\n",
       "      <td>9.663261</td>\n",
       "      <td>9.723703</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>8.847360</td>\n",
       "      <td>6.070738</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>8.597297</td>\n",
       "      <td>9.203618</td>\n",
       "      <td>9.257892</td>\n",
       "      <td>3.637586</td>\n",
       "      <td>8.932213</td>\n",
       "      <td>7.156177</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>10.000569</td>\n",
       "      <td>9.034080</td>\n",
       "      <td>10.457143</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>9.440738</td>\n",
       "      <td>8.396155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>7.759187</td>\n",
       "      <td>8.967632</td>\n",
       "      <td>9.382106</td>\n",
       "      <td>3.951244</td>\n",
       "      <td>8.341887</td>\n",
       "      <td>7.436617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>6.978214</td>\n",
       "      <td>9.177714</td>\n",
       "      <td>9.645041</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>8.696176</td>\n",
       "      <td>7.142827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>10.395650</td>\n",
       "      <td>9.728181</td>\n",
       "      <td>9.519735</td>\n",
       "      <td>11.016479</td>\n",
       "      <td>7.148346</td>\n",
       "      <td>8.632128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>8.402007</td>\n",
       "      <td>8.569026</td>\n",
       "      <td>9.490015</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>8.827321</td>\n",
       "      <td>7.239215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>9.060331</td>\n",
       "      <td>7.467371</td>\n",
       "      <td>8.183118</td>\n",
       "      <td>3.850148</td>\n",
       "      <td>4.430817</td>\n",
       "      <td>7.824446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>7.932721</td>\n",
       "      <td>7.437206</td>\n",
       "      <td>7.828038</td>\n",
       "      <td>4.174387</td>\n",
       "      <td>6.167516</td>\n",
       "      <td>3.951244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh      Milk    Grocery     Frozen  Detergents_Paper  Delicassen\n",
       "38    8.431853  9.663261   9.723703   3.496508          8.847360    6.070738\n",
       "57    8.597297  9.203618   9.257892   3.637586          8.932213    7.156177\n",
       "65    4.442651  9.950323  10.732651   3.583519         10.095388    7.260523\n",
       "145  10.000569  9.034080  10.457143   3.737670          9.440738    8.396155\n",
       "175   7.759187  8.967632   9.382106   3.951244          8.341887    7.436617\n",
       "264   6.978214  9.177714   9.645041   4.110874          8.696176    7.142827\n",
       "325  10.395650  9.728181   9.519735  11.016479          7.148346    8.632128\n",
       "420   8.402007  8.569026   9.490015   3.218876          8.827321    7.239215\n",
       "429   9.060331  7.467371   8.183118   3.850148          4.430817    7.824446\n",
       "439   7.932721  7.437206   7.828038   4.174387          6.167516    3.951244"
      ]
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Detergents_Paper':\n"
     ]
    },
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       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
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       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
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       "      <th>161</th>\n",
       "      <td>9.428190</td>\n",
       "      <td>6.291569</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>6.995766</td>\n",
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       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612    6.882437\n",
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     "text": [
      "Data points considered outliers for the feature 'Delicassen':\n"
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       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
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       "      <th>109</th>\n",
       "      <td>7.248504</td>\n",
       "      <td>9.724899</td>\n",
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       "      <td>6.511745</td>\n",
       "      <td>6.728629</td>\n",
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       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
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       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8.034955</td>\n",
       "      <td>8.997147</td>\n",
       "      <td>9.021840</td>\n",
       "      <td>6.493754</td>\n",
       "      <td>6.580639</td>\n",
       "      <td>3.583519</td>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>10.519646</td>\n",
       "      <td>8.875147</td>\n",
       "      <td>9.018332</td>\n",
       "      <td>8.004700</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>1.098612</td>\n",
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       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
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       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>10.514529</td>\n",
       "      <td>10.690808</td>\n",
       "      <td>9.911952</td>\n",
       "      <td>10.505999</td>\n",
       "      <td>5.476464</td>\n",
       "      <td>10.777768</td>\n",
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       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>5.789960</td>\n",
       "      <td>6.822197</td>\n",
       "      <td>8.457443</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>5.811141</td>\n",
       "      <td>2.397895</td>\n",
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       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>7.798933</td>\n",
       "      <td>8.987447</td>\n",
       "      <td>9.192075</td>\n",
       "      <td>8.743372</td>\n",
       "      <td>8.148735</td>\n",
       "      <td>1.098612</td>\n",
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       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>6.368187</td>\n",
       "      <td>6.529419</td>\n",
       "      <td>7.703459</td>\n",
       "      <td>6.150603</td>\n",
       "      <td>6.860664</td>\n",
       "      <td>2.890372</td>\n",
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       "      <th>233</th>\n",
       "      <td>6.871091</td>\n",
       "      <td>8.513988</td>\n",
       "      <td>8.106515</td>\n",
       "      <td>6.842683</td>\n",
       "      <td>6.013715</td>\n",
       "      <td>1.945910</td>\n",
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       "      <th>285</th>\n",
       "      <td>10.602965</td>\n",
       "      <td>6.461468</td>\n",
       "      <td>8.188689</td>\n",
       "      <td>6.948897</td>\n",
       "      <td>6.077642</td>\n",
       "      <td>2.890372</td>\n",
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       "      <th>289</th>\n",
       "      <td>10.663966</td>\n",
       "      <td>5.655992</td>\n",
       "      <td>6.154858</td>\n",
       "      <td>7.235619</td>\n",
       "      <td>3.465736</td>\n",
       "      <td>3.091042</td>\n",
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       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>7.431892</td>\n",
       "      <td>8.848509</td>\n",
       "      <td>10.177932</td>\n",
       "      <td>7.283448</td>\n",
       "      <td>9.646593</td>\n",
       "      <td>3.610918</td>\n",
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       "         Fresh       Milk    Grocery     Frozen  Detergents_Paper  Delicassen\n",
       "66    2.197225   7.335634   8.911530   5.164786          8.151333    3.295837\n",
       "109   7.248504   9.724899  10.274568   6.511745          6.728629    1.098612\n",
       "128   4.941642   9.087834   8.248791   4.955827          6.967909    1.098612\n",
       "137   8.034955   8.997147   9.021840   6.493754          6.580639    3.583519\n",
       "142  10.519646   8.875147   9.018332   8.004700          2.995732    1.098612\n",
       "154   6.432940   4.007333   4.919981   4.317488          1.945910    2.079442\n",
       "183  10.514529  10.690808   9.911952  10.505999          5.476464   10.777768\n",
       "184   5.789960   6.822197   8.457443   4.304065          5.811141    2.397895\n",
       "187   7.798933   8.987447   9.192075   8.743372          8.148735    1.098612\n",
       "203   6.368187   6.529419   7.703459   6.150603          6.860664    2.890372\n",
       "233   6.871091   8.513988   8.106515   6.842683          6.013715    1.945910\n",
       "285  10.602965   6.461468   8.188689   6.948897          6.077642    2.890372\n",
       "289  10.663966   5.655992   6.154858   7.235619          3.465736    3.091042\n",
       "343   7.431892   8.848509  10.177932   7.283448          9.646593    3.610918"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38, 57, 65, 65, 66, 66, 75, 75, 81, 86, 95, 96, 98, 109, 128, 128, 137, 142, 145, 154, 154, 154, 161, 171, 175, 183, 184, 187, 193, 203, 218, 233, 264, 285, 289, 304, 305, 325, 338, 343, 353, 355, 356, 357, 412, 420, 429, 439]\n",
      "[65, 66, 75, 128, 154]\n",
      "(440, 6)\n",
      "(435, 6)\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "# Keep outlier indices in a list and examine after looping thru the features\n",
    "idx = []\n",
    "\n",
    "\n",
    "# For each feature find the data points with extreme high or low values\n",
    "for feature in log_data.keys():\n",
    "    \n",
    "    # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n",
    "    Q1 = np.percentile(log_data[feature], 25)\n",
    "    \n",
    "    # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    Q3 = np.percentile(log_data[feature], 75)\n",
    "    \n",
    "    # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n",
    "    step = 1.5*(Q3 - Q1)\n",
    "    \n",
    "    # Display the outliers\n",
    "    print(\"Data points considered outliers for the feature '{}':\".format(feature))\n",
    "    display(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))])\n",
    "    \n",
    "    # Gather the indexes of all the outliers\n",
    "    idx += log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))].index.tolist()\n",
    "\n",
    "print(sorted(idx))\n",
    " \n",
    "# OPTIONAL: Select the indices for data points you wish to remove\n",
    "outliers  = []\n",
    "\n",
    "#outliers = list(unique_everseen(idx))\n",
    "\n",
    "import collections\n",
    "\n",
    "outliers = [item for item, count in collections.Counter(idx).items() if count > 1]\n",
    "\n",
    "\n",
    "print(sorted(outliers))\n",
    "\n",
    "# Remove the outliers, if any were specified\n",
    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)\n",
    "\n",
    "\n",
    "print(log_data.shape)\n",
    "print(good_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "Are there any data points considered outliers for more than one feature? Should these data points be removed from the dataset? If any data points were added to the outliers list to be removed, explain why. \n",
    "\n",
    "An outlier is an observation point that is distant from other observations. An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set.\n",
    "\n",
    "Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution. In the former case one wishes to discard them or use statistics that are robust to outliers, while in the latter case they indicate that the distribution has high skewness and that one should be very cautious in using tools or intuitions that assume a normal distribution. \n",
    "\n",
    "A frequent cause of outliers is a mixture of two distributions, which may be two distinct sub-populations, or may indicate 'correct trial' versus 'measurement error'; this is modeled by a mixture model.\n",
    "\n",
    "Also as per the box plot (above) and method IQR shows there are many outliers As per IQR Find the inter quartile range, which is IQR = Q3 - Q1, where Q3 is the third quartile and Q1 is the first quartile. Then find these two numbers: a) Q1 - 1.5IQR b) Q3 + 1.5IQR The point is an outlier if < a or > b.\n",
    "\n",
    "If any data points were added to the outliers list to be removed, explain why.\n",
    "\n",
    "We can see that with this method 1/2 of the points are considered as outliers and I don't like to remove so many data points so I have implemented another method Z score (based on standard deviation), I have considered the data outside the 2sigma as outliers this will remove 62 outlier points from dataset.\n",
    "\n",
    "we have to sometime careful as Outliers, being the most extreme observations, may include the sample maximum or sample minimum, or both, depending on whether they are extremely high or low. However, the sample maximum and minimum are not always outliers because they may not be unusually far from other observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Transformation\n",
    "In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers.\n",
    "\n",
    "### Implementation: PCA\n",
    "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the good_data to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the explained variance ratio of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data.\n",
    "In the code block below, you will need to implement the following:\n",
    "\n",
    "* Import sklearn.decomposition.PCA and assign the results of fitting PCA in six dimensions with good_data to pca.\n",
    "* Apply a PCA transformation of the sample log-data log_samples using pca.transform, and assign the results to pca_samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import visuals as vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " [ 0.44302505  0.70681723  0.82988103  0.93109011  0.97959207  1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Arr32Wnr16sWHH37IyJEj+cY3vkHv3r15//33GT58OJdddhkXXnghF1xwAVOm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\nktTht5erLNWzHxeaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13883e71ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "\n",
    "# TODO: Apply PCA to the good data with the same number of dimensions as features\n",
    "\n",
    "pca = PCA(n_components=6).fit(good_data)\n",
    "\n",
    "# TODO: Apply a PCA transformation to the sample log-data\n",
    "pca_samples = pca.transform(log_samples)\n",
    "#pca.fit(log_samples)\n",
    "\n",
    "# Generate PCA results plot\n",
    "pca_results = vs.pca_results(good_data, pca)\n",
    "\n",
    "# cumulative explaned variance\n",
    "print('\\n', np.cumsum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Explained Variance</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dimension 1</th>\n",
       "      <td>0.4430</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>-0.4014</td>\n",
       "      <td>-0.4381</td>\n",
       "      <td>0.1782</td>\n",
       "      <td>-0.7514</td>\n",
       "      <td>-0.1499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 2</th>\n",
       "      <td>0.2638</td>\n",
       "      <td>-0.6859</td>\n",
       "      <td>-0.1672</td>\n",
       "      <td>-0.0707</td>\n",
       "      <td>-0.5005</td>\n",
       "      <td>-0.0424</td>\n",
       "      <td>-0.4941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 3</th>\n",
       "      <td>0.1231</td>\n",
       "      <td>-0.6774</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>-0.0195</td>\n",
       "      <td>0.3150</td>\n",
       "      <td>-0.2117</td>\n",
       "      <td>0.6286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 4</th>\n",
       "      <td>0.1012</td>\n",
       "      <td>-0.2043</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.0557</td>\n",
       "      <td>0.7854</td>\n",
       "      <td>0.2096</td>\n",
       "      <td>-0.5423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 5</th>\n",
       "      <td>0.0485</td>\n",
       "      <td>-0.0026</td>\n",
       "      <td>0.7192</td>\n",
       "      <td>0.3554</td>\n",
       "      <td>-0.0331</td>\n",
       "      <td>-0.5582</td>\n",
       "      <td>-0.2092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 6</th>\n",
       "      <td>0.0204</td>\n",
       "      <td>0.0292</td>\n",
       "      <td>-0.5402</td>\n",
       "      <td>0.8205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>-0.1824</td>\n",
       "      <td>0.0197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Explained Variance   Fresh    Milk  Grocery  Frozen  \\\n",
       "Dimension 1              0.4430  0.1675 -0.4014  -0.4381  0.1782   \n",
       "Dimension 2              0.2638 -0.6859 -0.1672  -0.0707 -0.5005   \n",
       "Dimension 3              0.1231 -0.6774  0.0402  -0.0195  0.3150   \n",
       "Dimension 4              0.1012 -0.2043  0.0128   0.0557  0.7854   \n",
       "Dimension 5              0.0485 -0.0026  0.7192   0.3554 -0.0331   \n",
       "Dimension 6              0.0204  0.0292 -0.5402   0.8205  0.0205   \n",
       "\n",
       "             Detergents_Paper  Delicassen  \n",
       "Dimension 1           -0.7514     -0.1499  \n",
       "Dimension 2           -0.0424     -0.4941  \n",
       "Dimension 3           -0.2117      0.6286  \n",
       "Dimension 4            0.2096     -0.5423  \n",
       "Dimension 5           -0.5582     -0.2092  \n",
       "Dimension 6           -0.1824      0.0197  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dimension 1    0.4430\n",
       "Dimension 2    0.7068\n",
       "Dimension 3    0.8299\n",
       "Dimension 4    0.9311\n",
       "Dimension 5    0.9796\n",
       "Dimension 6    1.0000\n",
       "Name: Explained Variance, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DataFrame of results\n",
    "display(pca_results)\n",
    "\n",
    "# DataFrame\n",
    "display(type(pca_results))\n",
    "\n",
    "# Cumulative explained variance should add to 1\n",
    "display(pca_results['Explained Variance'].cumsum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5\n",
    "How much variance in the data is explained in total by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending.\n",
    "\n",
    "Hint: A positive increase in a specific dimension corresponds with an increase of the positive-weighted features and a decrease of the negative-weighted features. The rate of increase or decrease is based on the indivdual feature weights.\n",
    "\n",
    "## Answer:\n",
    "70.68% of the variance in the data is explained by the first and second principal components.\n",
    "93.11% of the variance in the data is explained by the first four principal components.\n",
    "\n",
    "Components breakdown:\n",
    "\n",
    "* The first principal component (PC1):\n",
    "  1. An increase in PC1 is associated with large increases in \"Milk\", \"Grocery\" and \"Detergents_Paper\" spending.\n",
    "  2. These features best represent PC1.\n",
    "  3. This is in line with our initial findings where the 3 features are highly correlated.\n",
    "* The second principal component (PC2):\n",
    "  1. An increase in PC2 is associated with large increases in \"Fresh\", \"Frozen\" and \"Delicatessen\" spending.\n",
    "  2. These features best represent PC2.\n",
    "  3. This makes sense as PC1 represents different features. And in PC2, the features in PC1 have very small positive weights.\n",
    "* The third principal component (PC3):\n",
    "  1. An increase in PC3 is associated with a large increase in \"Delicatessen\" and a large decrease in \"Fresh\" spending.\n",
    "  2. These features best represent PC3.\n",
    "* The fourth principal component (PC4):\n",
    "  1. An increase in PC4 is associated with a large increasing in \"Frozen\" and a large decrease in \"Delicatessen\" spending.\n",
    "  2. These features best represent PC4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation\n",
    "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions. Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "      <th>Dimension 3</th>\n",
       "      <th>Dimension 4</th>\n",
       "      <th>Dimension 5</th>\n",
       "      <th>Dimension 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.9434</td>\n",
       "      <td>3.8210</td>\n",
       "      <td>-2.7013</td>\n",
       "      <td>2.8132</td>\n",
       "      <td>3.0338</td>\n",
       "      <td>0.4677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6302</td>\n",
       "      <td>-2.0843</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.6619</td>\n",
       "      <td>0.1491</td>\n",
       "      <td>-0.4524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.7186</td>\n",
       "      <td>0.3198</td>\n",
       "      <td>1.3868</td>\n",
       "      <td>-0.7698</td>\n",
       "      <td>0.3031</td>\n",
       "      <td>-0.3414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0      -0.9434       3.8210      -2.7013       2.8132       3.0338   \n",
       "1       1.6302      -2.0843       0.2416       0.6619       0.1491   \n",
       "2      -2.7186       0.3198       1.3868      -0.7698       0.3031   \n",
       "\n",
       "   Dimension 6  \n",
       "0       0.4677  \n",
       "1      -0.4524  \n",
       "2      -0.3414  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after having a PCA transformation applied\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensionality Reduction\n",
    "\n",
    "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the cumulative explained variance ratio is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    "* Assign the results of fitting PCA in two dimensions with good_data to pca.\n",
    "* Apply a PCA transformation of good_data using pca.transform, and assign the results to reduced_data.\n",
    "* Apply a PCA transformation of log_samples using pca.transform, and assign the results to pca_samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca.fit(log_data)\n",
    "\n",
    "reduced_data = pca.transform(log_data)\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.9845</td>\n",
       "      <td>3.8525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6203</td>\n",
       "      <td>-2.1223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.7336</td>\n",
       "      <td>0.2403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0      -0.9845       3.8525\n",
       "1       1.6203      -2.1223\n",
       "2      -2.7336       0.2403"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation: Dimensionality Reduction\n",
    "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the cumulative explained variance ratio is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    "\n",
    "* Assign the results of fitting PCA in two dimensions with good_data to pca.\n",
    "* Apply a PCA transformation of good_data using pca.transform, and assign the reuslts to reduced_data.\n",
    "* Apply a PCA transformation of the sample log-data log_samples using pca.transform, and assign the results to pca_samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2).fit(good_data)\n",
    "\n",
    "# TODO: Apply a PCA transformation the good data\n",
    "reduced_data = pca.transform(good_data)\n",
    "\n",
    "# TODO: Apply a PCA transformation to the sample log-data\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Create a DataFrame for the reduced data\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation\n",
    "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.9434</td>\n",
       "      <td>3.8210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6302</td>\n",
       "      <td>-2.0843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.7186</td>\n",
       "      <td>0.3198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0      -0.9434       3.8210\n",
       "1       1.6302      -2.0843\n",
       "2      -2.7186       0.3198"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Dimension 1  Dimension 2\n",
      "0    -1.757983     0.009711\n",
      "1    -1.788665    -0.812251\n",
      "2    -1.883353    -1.599135\n",
      "3     1.155265    -1.405201\n",
      "4    -0.784786    -2.394294\n",
      "5    -1.085043    -0.324315\n",
      "6    -1.128640     0.262863\n",
      "7    -1.567236    -0.901014\n",
      "8    -0.863567     0.664968\n",
      "9    -2.873382    -0.677438\n"
     ]
    }
   ],
   "source": [
    "# Display sample log-data after applying PCA transformation in two dimensions\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))\n",
    "print(reduced_data[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale. \n",
    "\n",
    "### Question 6\n",
    "What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?\n",
    "\n",
    "What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm?\n",
    "\n",
    "K Means clustering* is a quick and conceptually straightforward algorithm for clustering data. It works well when the data clusters are relatively simple in shape, like Gaussian hyperspheres, but can struggle to identify clusters properly when the clusters have more complex non-linear geometries.\n",
    "Gaussian Mixture Models is an generalization of K Means clustering that takes into account the covariance of the data. It os very fast, and it does not presume the data has a specific structure that may in fact not be applicable. (From http://scikit-learn.org/stable/modules/)\n",
    "\n",
    "Now, lets imagine some unclustered data. K-means/Mixture of Gaussians tries to break them into clusters. Let's says we are aiming to break them into three clusters. K means will start with the assumption that a given data point belongs to one cluster. Choose a data point. At a given point in the algorithm, we are certain that a point belongs to a cluster 1. In the next iteration, we might revise that belief, and be certain that it belongs to the cluster 2. However, remember, in each iteration, we are absolutely certain as to which cluster the point belongs to. This is the \"hard assignment\".\n",
    "What if we are uncertain? What if we think, well, I can't be sure, but there is 70% chance it belongs to the cluster 1, but also 10% chance its in Cluster 2, 20% chance it might be Cluster 3. That's a soft assignment. The Mixture of Gaussian model helps us to express this uncertainty. It starts with some prior belief about how certain we are about each point's cluster assignments. As it goes on, it revises those beliefs. But it incorporates the degree of uncertainty we have about our assignment.\n",
    "Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?\n",
    "I have manually checked both the algos in our dataset against silhouette_score and it came out K-means is performing better than GMM. K-means is an effective algo. to extract a given number of clusters from a training set and in our case it is doing better than GMM clustering.\n",
    "\n",
    "GMM clustering is more flexible but need not to be the more accurate than K-means because you can view it as a fuzzy or soft clustering method. Soft clustering methods assign a score to a data point for each cluster. The value of the score indicates the association strength of the data point to the cluster. As opposed to hard clustering methods, soft clustering methods are flexible in that they can assign a data point to more than one cluster. When clustering with GMMs, the score is the posterior probability. For an example of soft clustering using GMM.\n",
    "\n",
    "### Implementation: Creating Clusters\n",
    "Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known a priori, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the \"goodness\" of a clustering by calculating each data point's silhouette coefficient. The silhouette coefficient for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the mean silhouette coefficient provides for a simple scoring method of a given clustering.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    "\n",
    "* Fit a clustering algorithm to the reduced_data and assign it to clusterer.\n",
    "* Predict the cluster for each data point in reduced_data using clusterer.predict and assign them to preds.\n",
    "* Find the cluster centers using the algorithm's respective attribute and assign them to centers.\n",
    "* Predict the cluster for each sample data point in pca_samples and assign them sample_preds.\n",
    "* Import sklearn.metrics.silhouette_score and calculate the silhouette score of reduced_data against preds.\n",
    "* Assign the silhouette score to score and print the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 clusters: 0.36313\n",
      "6 clusters: 0.36249\n",
      "5 clusters: 0.35221\n",
      "4 clusters: 0.3339\n",
      "3 clusters: 0.39689\n",
      "2 clusters: 0.42628\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13887746c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "#keep the scores for each cluster size\n",
    "sil_scores = []\n",
    "\n",
    "random_state = 7\n",
    "\n",
    "for i in range(7,1,-1):\n",
    "    clusterer = KMeans(i, random_state=random_state).fit(reduced_data)\n",
    "    # TODO: Predict the cluster for each data point\n",
    "    preds = clusterer.predict(reduced_data)\n",
    "\n",
    "    # TODO: Find the cluster centers\n",
    "    centers = clusterer.cluster_centers_\n",
    "\n",
    "    # TODO: Predict the cluster for each transformed sample data point\n",
    "    sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "    # TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "    score = silhouette_score(reduced_data, preds)\n",
    "    sil_scores.append(score)\n",
    "    print(i, 'clusters:', score.round(5))\n",
    "\n",
    "# plot the scores\n",
    "import matplotlib.pyplot as plt\n",
    "_ = plt.plot(np.arange(7,1,-1), sil_scores, '-o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 7\n",
    "Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score? \n",
    "\n",
    "Silhouette refers to a method of interpretation and validation of consistency within clusters of data. The technique provides a succinct graphical representation of how well each object lies within its cluster. \n",
    "The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from -1 to 1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters.\n",
    "\n",
    "The silhouette score (SS) comes out to be 7 clusters: 0.35491 6 clusters: 0.36365 5 clusters: 0.35319 4 clusters: 0.33115 3 clusters: 0.36399 2 clusters: 0.44716\n",
    "The best silhouette score is for 2 clusters whihc is obvious as seperating any data points with a plane is the easiest but our underline data in question is more complex than the 2 clusters and if you read carefully on the grouping (G1, G2, G3, G4, G5 and G6) are the most logical groups based on total spending profile of individual cutstomers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster Visualization\n",
    "Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q1NRU1PsVCImIiIiIxGD16tW0tLQwevRozCzu5tQU5xyvvvoqq1evZsyYMUVt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\nf6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13887241358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the results of the clustering from implementation\n",
    "vs.cluster_results(reduced_data, preds, centers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing a Biplot\n",
    "\n",
    "A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case Dimension 1 and Dimension 2). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.\n",
    "\n",
    "Run the code cell below to produce a biplot of the reduced-dimension data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1388796d978>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RO/18Epo9/yB0aondAL4N4Jr84EZBPAadiuGz0GbHL0GDw0uNMfvzzxHoOcuWXUQxZ4fV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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138877154a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.biplot(log_data, reduced_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "Frozen, Fresh and Delicassen are the features most strongly correlated with the Dimension 1.\n",
    "\n",
    "Detergents_Paper, Grocery, and Milk are the features most strongly correlated with the Dimension 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    "* Fit a clustering algorithm to the reduced_data and assign it to clusterer.\n",
    "* Predict the cluster for each data point in reduced_data using clusterer.predict and assign them to preds.\n",
    "* Find the cluster centers using the algorithm's respective attribute and assign them to centers.\n",
    "* Predict the cluster for each sample data point in pca_samples and assign them sample_preds.\n",
    "* Import sklearn.metrics.silhouette_score and calculate the silhouette score of reduced_data against preds.\n",
    "* Assign the silhouette score to score and print the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette coefficient for 2 clusters: 0.422\n"
     ]
    }
   ],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "clusterer = GaussianMixture(n_components=2, covariance_type='full')\n",
    "clusterer.fit(reduced_data)\n",
    "\n",
    "# Predict the cluster for each data point\n",
    "preds = clusterer.predict(reduced_data)\n",
    "\n",
    "# Find the cluster centers\n",
    "centers = clusterer.means_\n",
    "\n",
    "# Predict the cluster for each transformed sample data point\n",
    "sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "# Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "score = silhouette_score(reduced_data, preds)\n",
    "print(\"Silhouette coefficient for {} clusters: {:.3f}\".format(2, score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tO/xyERGpanEon41z7mHg4YibISKSszCoScz8dDnHPZvX91hPQVDM\nNbd1B0GhDgdPtkXSHBERKR9lhERE8rQzM5Smu1udmYKguBvXCPVJy+rNT04pIiJVTYGQiEgBJk+e\nzDl7DE752jl7DFYQFHczh8PA/t3BUL3BwH5+uUi1UEEQkZQUCImIFGDBggXcu2VDytfu3bIh6zxD\nUkK5XPwNa4Cnm2D6EJ8Fmj7YPx/WUP72ipSCCoKIpBWLMUIiIpVoZ3W4NCWxu5xjypQpAMoMlVt4\n8RcWQljWBvNfTR3kDGuAGw6JpJkiJZepIIjOe6lxygiJiOQhZYlsMz4xaEiPMUPp5hmSElM1OBFP\nBUFE0lIgJJKv1g0wYxaMneQfW1N3j5Lqk26eoPm3387db65j/u23Z5xnSMpAF38ingqCiKSlQEgk\nH60b4KjxMGchLFnuH48ar2CoBjjnmDdvXsZ5gtLNMzRv3jxcmm50UmTlvPjTQHSJMxUEEUlLgZBI\nPmbfAlu3Q0enf97R6Z/PviXadknJmRl33nknp59+OpB+nqDkYOj000/nzjvvxNKU2pYiK9fFnwai\nS9ypIIhIWiqWIJKP5uXdQVCooxOeXB5Ne6SsGhoauPvuu5kwYQIXXnhh2kII4fJ58+Zx55130tCg\nC4+yCS/+Zq/x3eHGNvogqNgXfxqILpVABUFEUlIgJJKPcWNg2aqewVB9fxg7Jro2SVk1NDRw3333\nZc3wTJ48mUmTJikTFIVyXPxpLJKISMVS1ziRfMycBgMH+OAH/OPAAX651IxcgxsFQVVMA9FFRCqW\nAiGRfAwbDE/fBdMn+izQ9In++bDBUbdMRMpJA9FFRCqWusaJ5GvYYLjhW1G3QkSiVK6xSCIiUnQK\nhERERAqhgegiIhVJXeNERERERKTmKBASEREREZGao0BIRERERERqjgIhERERERGpOQqEpLK1boAZ\ns2DsJP/YuiHqFomIiIhIBVDVOKlcrRvgqPGwdTt0dMKyVTD/Ps3nIyIiIiJZKSMklWv2Ld1BEPjH\nrdv9chERERGRDBQISeVqXt4dBIU6OuHJ5dG0R0REREQqhgIhqVzjxkB9Uu/O+v4wdkw07RERERGR\niqFASCrXzGkwcEB3MFTf3z+fOS3adomIiIhI7CkQkso1bLAvjDB9os8CTZ+oQgkiIiIikhNVjZPK\nNmww3PCtqFshIiIiIhVGGSEREREREak5CoRERESkdFrbYcZqGLvUP7a2R90iERFAXeNEREQkV63t\nMHsNNLfBuEaYORyGNWRe/6gW2NoJHcCyNpj/KjzdlPl9IiJloIyQiIiIZBcGNXPWw5I2/3hUS+YM\nz+w13UEQ+MetO/xyEZGIKRASqTWtG2DGLBg7yT+2boi6RSJSCfIJaprbutcPdTh4sq1EjRQRyZ26\nxonUktYNcNR42LodOjph2SqYf5/KjotIdvkENeMafXe4xPfVG4xtLEULRUT6RBkhkWKLc8Zl9i3d\nQRD4x63b/XIRkUzGNUJ90rJsQc3M4TCwf/f76g0G9vPLRUQipoyQ1K7WDT4AaF4O48bAzGmFZ0Xi\nnnFpXt4dBIU6OuHJ5dG0R0Qqx8zhvtBB2D0ul6BmWIMvjDB7jc8cjc2hwIKISJkoEJLaVKqAJVPG\nJQ4Tv44b4/c1MRiq7w9jx0TXJhGpDPkGNcMa4IZDytNGEZE+UNc4qU2l6iIW94zLzGkwcIAPfsA/\nDhzgl4uIZBMGNc3H+kdldkSkgikQktpUqoBl3JjuICMUp4zLsME+6zV9om/T9Inx6bYnIiIiUkbq\nGie1qdhdxMLxRo8uhTqD/v2gc0c8My7DBsejm56IiIhIhBQISW2aOc2PCQq7xxUSsCSPN+rfD/rV\nwaiD4YRjilOEIdNnF7vgg4iIiEgNUCAktSnsIjb7Ft8dbmwBQUTyeKPOHWDmg6BSZl7iXqFORCRu\nWtt9sYfmNl8OXBXsRGqaAiGpXcXqIhZVgYS4V6gTEYmT1nY4qqW7/PeyNl8O/OkmBUMiNUrFEkQK\nVa4CCckTtT66NN4V6kRE4mT2mu4gCPzj1h1+ebm0tsOM1TB2qX9sbS/fZ4tIL8oIiRSqmOON0knV\nDS6xKEMoThXqRETipLmtOwgKdTg/J1I5KCMlEjvKCIkUqhwlqVN1g+tyvihDrnMCJWeUWjcUr30i\nInE3rhHqk5bVm58YthzikJESkR6UERIphkLHG2Wr/pZuHNJRh/miDNkKPqiwgojUupnDfQYmDEbq\nDQb288vLIeqMlIj0okBIJJ1ylabOJUhJN+9RrpXpVFghflT6XKS8hjX4bmiz1/jgY2yZq8aNa/Td\n4RKDoXJmpESkF3PORd2GnDU1NbmWlpaomyG1IDk4CbudlSKDMmMWzFnYO8iZPrE7SMnUHsh+QT12\nEixJUURh7BhovqO4+yPZlfP8EpF4SB4jFGakNEZIpOjMbKlzrinbehojJJJKpgxKseVSfjvdOCTw\nF9RzFvpAZ85C/zx5/E+5KttJbsp5folIPIQZqelDfBZo+mAFQSIRU9c4kVTKOTdQum5vyUFKqnFI\nM2bl1uWtHJXtJHdRzT0lItEa1gA3HBJ1K0QkoIyQSCrlzKDMnOaDklyrvyXK9YK6HJXtJHfK0ImI\niEROgZBIKoUEJ31VSJDSlwvqMKPUfId/VBAUnXKeX5IfTXwpIlL1VCxBJJ2wqle20tRRKuage1Ux\nK69KOL9qVa9B7cDA/hrPISJSIXItlqBASCSVSgoKinFBrSpmtaWSzu8ozFgNc9b3LnM8fbDGd4iI\nVIBcAyEVSxBJVmmTjxY6mStUzjxDuoAvXKWd34la2/0cMM1tfk6WUs0Bo4kvRURqgsYIiSSrxdLG\nlVDFLLyAz1YqXDKr1PM77K42Zz0safOPR7WUZuzOuEbfHS6RJr4UEak6CoREksUlKGjd4Mtjj53k\nH0t5wV8JVcwq9QI+k3J+x6G4nN99NXtN95gd8I9bd/jlxTZzuB8TFAZD4cSXM4cX/7NERCQyCoRE\nksUhKCh39qMSqphV6gV8OlFluGJxfudRkS1Td7ViV3jTxJciIjVBgZBIsjgEBeXOflTCPENxuIAv\npqgyXFGf3/l2cUvXXe3wAaXpMhdOfNl8rH9UECQiUnUUCEn0ougelEmpgoK+7GcU2Y+4zzMU9QV8\nsUWV4Yo66M23i1u67mpQvi5zIiJSVVQ1TqIV1wpWxajElqiv+zlujF8n8UK5krMfxRBewFfL3DtR\nfsfFPr/7It+KbMMa4MEx8Lnn4MV2OKABbj4UZvxTFd5ERCQvyghJtOI0AL6Umam+7GfrBmjbBl1d\nUGd+WaVnP4ol7lmrvqi2DFeu8q3I1toOZyyHZ7fDti7/eMZyOGKAKryJiEheIguEzGyYmT1kZqvM\n7Bkz+3JUbZEIxWUAfKkHrue6n2E7br8fdnSBA/rVwQVnRZ8lk+KKuotaVPKtyJauSx3UVoW3YheG\nEBGpYVF2jesELnfOPWVmjcBSM/uDc25lhG2ScotLF7BSTyia634mt8M5qOsHjbtV9wVyrU6UGmUX\ntaiEFdlmr/Hd18bmODFqui51q7bnt71KFBaaCAPCZW0w/1VVtBMRyVNkgZBzbgOwIfi5zcxWAfsD\nCoRqycxpfqxMePEfVfegUmemct3PuGTIyimu48SkdMKKbH0xrtFf+CcGQ2EXuHy2V4kyFZqohf0X\nESmyWIwRMrMRwAeA5hSvfd7MWsysZdOmTeVumpRaXLoHlbo0c677WQkloos9lipO48Skpzh1w9Ik\np/kXmhARkZTMORdtA8wGAo8A33XO/SbTuk1NTa6lpaU8DZPakpyVCDM25Q7K4tKOcrZv7CQ/LqvX\n8jG+KIJEI7kbVj0+EImyG1Zre210gUtnxmo/T1JyVmz6YGWEREQSmNlS51xTtvUizQiZWT1wJzA/\nWxAkUlJxyUzFpR3JwixQ0/mwua242ZtKyILVonzn+ymlWp/kVFkxEZGiiiwjZGYG/AJ4wzl3WS7v\nUUZIJALJWaBUCsnexD0LVqvGLoUlKbpcjW30gYj0FGarmtv8eKZSZatqPSsmIpKDXDNCUVaNOx64\nEFhuZsuCZVc45x6IsE0ixVPpldDC9i/8HWxpg640N00Kzd5U20Sp1SJTcQLpqZzV3GqlMISISBlE\nPkaoL5QRkopR6VmOXLJAUHn7VWqVHvwm6jVGKOiGpVLNvWnsjohIrFTEGCGRqlVIJbRiV2XLR3L7\nk9UZ7LdXfMYwxUGpJ+Utt3C+n+lDfBZo+mAFQemompuISEWKsmucSPXKdz6guMypk6r9oTAL1PLr\n6AKgOGZeSj0pbxRK1Q2rXONpykXdCEVEKpIyQiK56GuWJt9KaHGZUydV+81gv72jzwLFNfNSi5Ph\n5iPscjdnvS/GMGe9fx7lHEWFUjU3EZGKpEBIJJt8LrxnTvNZkzCYCLMoM6dl/qy4XEynav+gRmhZ\n6LMbUWZf4hIsJlMZ8NzEsSx3odSNUESkIikQEskmnwvvfOcDisvFdFznM4L4BIvJ8g1+a021jqep\n9TmOREQqkMYIiWST74X3sMF9Hxsyc5ofE5RcbS6Ki+l82l8O48b4sVOJ30kcMi8qA54bjacREZGY\nUEZIJJtiZmmyjTWKcyYmLuKceQmDx+Y7ou9CGFcaT5Naa7svwz12qX+s5DFTIiIVQvMIiWRTrDmB\nKn1uoTgJq8YVK/MSxyp01SysGvdkm88EVXrVuEL1mrMJHyxqnFH1qLZKiSIxl+s8QgqERHJRjAvv\nGbN8oYXkLl3TJ/bsgqaL8vJSgCpR04Ss1U2BrkjZ5RoIaYyQSC6KMV4ml7FGcZlHqJZU4/w/0q0S\n7sRXawEJ8TJVSoxjoFsJvzMiRaIxQiLlkm2sUesGOOsL8OZb8SsNna++zr8UhbhWoZPCVcqcReMa\nu8dMhVRAonpUUqBbKb8zIkWiQEikXDIN8g8zQctX935fpV6Ux3Xi02RxKVkuxVcpcxapgER1q6RA\nt1J+Z0SKRIGQSCH6kvHIVBEu7J6VSqVelMd14tNkca5CJ4WplDvxmpC1ulVSoFspvzMiRaIxQiL5\nymc8T7qxRqm6Z4Uq9aK8UrqcVev8P+rnH785izJ9J+GErFJ9wkC3Eiolxu13RqTEFAiJ5KuYg+xT\nTRIKMOYQuP+nxb0oL1dVurhOfJpKXCePzVdylaplbTD/1drLMswcDvM2QtsO6ML3gRhQF82d+Lh8\nJwqQo1EpgW74O/PWDnCAEd3vjEgZqGucSL6KmfFI1T1rz91LEwSVa9yOupxFR/38e3JJj1GIw3ei\ngfCSK0t6FKlSCoRE8lXMQfaZxg8VUznH7ZRrn6pRodX2ar2ff2u7n5unaanPBiUGQtu7ogkI4/Cd\nxCEYC4Xf0dil/lHBWG5Kfdxmr4HtQQYV/GNUvzMiZaCucVK4TF2tqnly0JnT/Jig5Ik48814lKN7\nVrnH7VRbl7NyKMZcUrXczz+5C1qyqALCOHwncQjGoLzdBKupK2A5jltczhGRMlFGSAqTqatVpZRP\nzlclZjwyZbHiPudP3NtXLMXI2lVSlapiS856JIsqIIzDdxKXMs7lykxVQlfAvmR4ynHc4nKOxJGy\nmFXJnIuy03TfNDU1uZaWlqibIYlmzPIBTvKA+OkT/c/pXlOWIBrJ2YYwi/XgHDhjeu/lcQns0rU7\nLu0rprGT/I2DXsvHQPMduW8nvBMe9ypVxTZ2qb/oTSUMPqIqGpHqO4HiZSyyZT+SMwqFHo98sy3p\nvqOxjdB8bN/bkc6M1T74Sc7CTR8cj8IFvb4PfLCc7vsox3Er9jlSLfr6XUnkzGypc64p23rKCElh\nMnW1qpTyybUkXRbrtnvjPedPpcxJVAzFGnsWVqlqPtY/1sp/1qnuaBuwX3308/MkfydQvIxFLtmP\nYs5XVEi2pVxZh7h38+prhqccx01zWqUWp/F1UlQaIySFyVYiuVLKJ9eSVON24h60xr19xZRt7Fk1\njXkohZnD/biJ5DvaLccWdpxKcdwzXVz1NWOR67YKKeOceAzad0BbJ4S/ln1pe7rvqK/dBLN9J3EY\nl5VJXwO1Yh23bCql1Hc5xT2olrwpEJLCZLtoK2YxASmduM/5E/f2FVOmCV7jMhdNnJVi8spSHfdi\nXlyV+kItWxGKvnxeMb6jXL6TcgUO+eproFZJE7NWm7gH1ZK3tGOEzGx34JvAUOBB59ztCa/9xDn3\nH+VpYjeNEYqpsDJc8kVbttfiqpor3aUT9zE4xWpfpX+3cR/zUK1KddyLud1Snxuptp+snOdirvsb\n57FyGo9TOfRdVZxcxwhlCoTuBJ4H/gZ8Bv/VX+Cce8fMnnLOHVPMBudCgZCUXNwDglKKe9BaaPuq\n4bst1yDzWpOti1WpjnsxL67y3VauXf4yFaGgwLbno1p+F6IO1NTVNndRf1fSJ7kGQpm6xh3onJsQ\n/Hy3mV0J/NnMzi1KC0XiKNOg/GqvdBf3OX8KbV81fLepumcAvL3D/yet/5T7LpcuVqXqFlPMrk75\nbKsvXf5SHgPgsAGwa7/yXxhWS1elKMfjqKtt32jsVFXKVDVuFzPb+bpz7rvATcCjwN6lbphIJGpp\nUH6tqYbvNpyLJvkW1qrt8ZsfpZwKmd8jl2pQpZwDqJjV/fq6rVT7/mYnnPWP3scw5THoD/cfGU1l\nwjjMy1TpVAlNJGMgdC9wcuIC59wvgMuBd0vZKJHIFKt0scRPNXy34V3/wwf0XN5J7V7AFDppZi5F\nBnItKZxvQBbVRI2p9h1geYrAOm5llePWnkqkSmgimlBVpIdqGEciqVXTd1vO8RFxH0PQ1yIByfvT\ntgNu31h4kYF8J1yMcqLGTAUQVISj+qn4ilQxTagqko90E45W2oWy9FZN3225JqQsNNtSDn25q51q\nf+5+DQb0K7yLVb7djKLsnhR2L0tFmYHsosrkFYu6F4poHiGRXgodlF/pJZqrWdwLQmQTZjMe3QJ1\nBv2d7xZXqguYYk74mau+ZqD6Mmg+1f5s74IL9oPGfoUVLMi3m1GU3ZPC7mVn/cN3h0tUiYUHyqka\nCg1oXiIRBUJS4eIWdCR3v1q2yk8qW6mZB4mP5Auv/kA/g1ED4IQ9SnMBU+6L9HwuLvsyaWa6/Vm1\nvWeXwvBOf1+6A+Zbxayc1c/SBZn3H5m69LYyA+lFcZOgFFQJTWpcToGQmf0bMCJxfefcL0vUJpHc\nxDHoqIYSzRJPyRdenYDhg6BSXciUu0RxPheXfbmrncv+5BqMJQcVn3pv7gFZor4EcoXItl/KDPSN\nCg2IVIWsgZCZzQMOBJYBO4LFDlAgJNGKY9BRDSWaq1ncMoh9EcWFV7ku0kP57mOud7Vz2Z9cgrF0\nQcWDY+C2jX0LJvoahORbvCLbftViZqCQQiDVMo+RSI3LJSPUBBzhKqm8nNSGOAYd48b4zFRiuyqt\nRHMpxCEAKXcGsdj7HIBgk6oAACAASURBVMWFV7kzBaXex1z2J5dgLF1QcdvG/IKJXIOQQsalVGMG\no5BAptAxPuW+SSAiJZFL1bgVwPtK3RCRPovjvDAzp/mSzGG7whLNM6dF16aohQHInIWwZLl/PGq8\nX15OmTKIxVaKfY6qwlMxJ/zMphz7mG1/cqnIF1VQUUiFuXJVGiyXXCoaZqrqVmi1Ps1jJFIVcgmE\n9gFWmtliM/tt+K/UDRPJKo5BRyWWaG7dADNmwdhJ/rHYAUo5A5BMyplBLMU+V8qFVyElheOwj7kE\nY1EFFYUEYNVWKjlbIJMtUCpGMFvOmwQiUhK5dI27qtSNEMlLGHTMvsVfzI6NyZiPSirRXI7uYnHp\nwljOboul2ufELlSlmui0mN2N/t4GN2+Aw/pQ2S7qsSq5dJ8rpFtUPsc3fM+/2n2BjMSO6rkGYNVW\nECFbIJNtTJTG+IgIOQRCzrlHzOy9wAeDRU86514tbbNEclRJQUcclaPgRFzGTc2c5oO8cH9LmUFM\ntc91BocfUJztl2oOk0K3++2XYEsndAXPO4FOB09vg5XbKmeelWzBWL5BRfLxbWmDn673FeeuHpn6\n/cnvSZQYgOUSYEUdZBZTtkAmW6CkMT4iQg5d48xsIvAkcD4wEWg2s/9T6oaJSBmUI1sTly6M5ey2\nOHMaDEi6CO1ycPefitP1sJDxDaUaN9Ha7osFdKV5va9jMKLQl259+XSLSj6+Dl+Ldd7G3uNb0r0H\n/P/c+9V3dx2E7ONlclVI18ZyytbVL1v3xTh0wxSRyOXSNe5K4INhFsjM9gX+CCwqZcNEpAzKka2J\nUxfGcmUQhw2G806BX/4WEgtubm/vzrYV0gUt3/EN2TI+hYybmL0mfRDU121FoVRZtkSpji/44xYG\niTOH9zwv/rKl93u6gBEJ2Z0Zq4szuWc5jkF43j+6xf9u1Fl+EwJny8rlkvGppgyZiOQll0CoLqkr\n3OvkVmRBpLbEoUR0X/W1u1i++1iLXRhXvtgzCILubFuhF5z5jm8o5biJ5raeY1dSybatXIPDUoyP\nymcy175KdXxDHc4HPfOTzos68/9TJyZuS1XFrtTHIDzv2zp77s8zeXabzBTIVNuYKBEpiVwCod+Z\n2WLgV8HzScADpWuSSAXKp+hAHAKnvmRr8i2sEIf9jEKmbFuhF5z5jm8o5biJIwb4blnpZNtWrsFh\nqbIW5SiJHR7fzZ29g8Z6890nk8+L/g76GZhL/50UEsCGQeVftsBz20t7DMLzPqk3Lp0UP+iE0mR8\nSlWkREQikUuxhK+Z2QTgeHy9mpucc3eVvGUilaSvRQfKPblnJrlma/IprBCn/Sy3TNm2CesLu+DM\n9253tgvmUtxFP3RX2KN/9m3lGhyWKmtRjipi4fH99kt+PJXDd3MLgxuz3udFJzAqqLqX+J2A7xLX\n3OaD0AH9YPuOvgWw6TI0ieoNDh/Q/VmFXPyn6xoI8e42GSpH10ERKatcMkI45+4E7ixxW0QqV1+L\nDpSjWlux5VNYoRL3sy8yZbsyZdvGtRV+0Z3P3e5SjptYuT318j36+4IC2eSSkWlth4WbSpO1KFcV\nsWENMPdwXyUuOeCcvQZWbet9Xhw90P8cZpHWvwNnLO95QT6gH1zwXli13QcuABOeyRy4pMvQhOqA\nAXVw92vdQVYhF/+ZugZWQunqcnSfFJGyShsImdljzrkPm1kbPZP4Bjjn3O4lb51Ipehr0YEsQYVz\nDjPL+rG5rtdXnZ2d3H///bS0tLB161YGDhxI0779Oat/P/p37uheMVthhbjMIVQKuWS70mXboird\n25eMT1+7ABWaUcn2/vBu/JYUV+3FuIgu95iSVAFnqvMiVSBy8wbY4boDmA5gexc09oNFo3LPWmTK\n0ADsUw9n7AW3byzOxX+4f8kZqHoqo3R1ObpPikhZpQ2EnHMfDh5jfotGsqrVMRrl1NeiAxkCp/b2\ndiZMmMCFF17I5MmT037kggULmDdvHnfeeScNDcW5WNu2bRvXXnstc+bMYe3atb1eH2rvYXrdnnyl\naxC71b8nexnsuMwhVAqFZLuiHMidS8Ynny5AhQZ32d4f3o1PrkxnFO8iOuoqYqnOi7YdvQORVFUp\nwgvyvmQtsmVoJu5b3Iv/xP37yxY/JirfqnFR0CSsIlXHXHJVo+QVzA4E1jrn3jGzk4AjgV865zaX\noX09NDU1uZaWlnJ/bGVLvmsdXqDXwhiNcgsDzlxKRKf5Xtqbf8V5M77A4sWLqaurY/78+SmDoQUL\nFjBlyhS6uro4/fTTufvuuwsOhl599VXOOusswt+xgw8+mIkTJ7LXXnvxxhtvsHDhQp5//nkAmnbb\nk/snfZ79rvpy9kIJ1Xr+jZ0ES1JktsaOgeY7yt+eYpqx2s9Fk3zBN31w5kAhzCLlG9xlev/YpamL\nMexXDy3Hxv8iOl/p9jtZ+P00t6Vef2xj7y6K6cYI9Qca+3cHLfmcC7kqRfGBUhU0SL5BEAbrGiMk\nEjtmttQ515R1vRwCoWVAEzACWAz8FjjUOXdmEdrZJwqE8jBjFsxZ2PuO/PSJ1TFGo5IlBU7ua5/h\n7C98jgce6C7K2CsYat3Ags9+mSm/X0RXwl3hM888k/vuuy/vbnLbtm3jpJNOoqWlhZEjRzJnzhwO\nO/bf+O79q/jTs69SZ8b4Dwxh3K4bufxLl/DSSy/R1NTEww8/zG677dan/ayajGQ1/26lu/hOdTFd\nLvkGZ3HT14v0VPudLPGCvK+BS2LVuFQZmlJe/PfaNn6S1L5uO/GYHjGgZ1fCfLeZ7bNUklsk1ooZ\nCD3lnDvGzL4GtDvnbjCzvzvnPlCsxuZKgVAeqvmudRVKzPSEdgZDx5/IgsM/wpRt/+zROyhT5iij\nhC6Ts+pe59vNf2LkyJE8/vjjDNxzH078wUO8sfVd6sxwOBxw8H4DuXXSIZzw4eN56aWXmDVrFlde\neWVR9r3iVHO2K1PQkTzhZ2LXtULuwGcLECrpbny6fcnnwj9xbFSqboH71vsubMmfkZjl2cXgkaNh\n3B6F7U+xL/4LzTymCnyM1KXJw22q/LVITShmINQMXAdcCZzjnHvJzFY450YXp6m5UyCUh2q+a12l\nUgZDZpxTvyf3vvtGzyAImP+x85m8eGHfPiThIr6zo4ORvMBaOvj973/Paaedxi8ef4nv3v8sdXVw\nx+eP443t7zJ93lL61xk3TjmGjn8t4/TTT2fYsGG8+OKL9O+fUwHK6lOt2a50QceDY3pWK6vHVyuD\nwu7A5xogVMLd+Ez7km83s9Z2aFoKr6ZIC6XK0jVvgROXwTvB/++JXd3idLzyyTwmH986egeIKT+r\nsXchiWJni0QkNnINhOpy2NbFwHHAd4MgaCRwW6ENlDKZOc3fpa4PLlSzDeKXyE2ePJn58+dTV9f9\n69nlHPekCoIYwuTNabrDtW7wgfDYSf6xdUP3awkD/e9nK2vp4JBDDuGUU04B4KFnN/Huji4+fNA+\nHDVsEB89dD/ev9cAtr+7g8f/+RqnnnoqBx98MK2trT268tWcsCpc8x3+sRqCIOge1D59iL+AnD7Y\nP79tY++B+G/t8AP6Uw3Oz1WmAf7J7brhEH+RfMMh8bx4zbQv6QoP/GWLz46MXeofW9t7rjOswWd9\n6pPem26g/m0bfTe3UOKEpXEyrjH3fQolH99cgqBwm7meZyJSM7IGQs65lc65S51zvwqev+Scu6b0\nTZOiCOcymT7R37GePrE6uu5UuZ3BUJoxPzuDoPq9U1dgCzM+cxb6rpFzFvrnYTCUUNa6hbcBOP/8\n83cGXys3vAXAmP27u9Ic8l5/cbL0X5upq6vj/PPP9+//80MF72/JZAoGy/H+SpYq6Eh1IR9OCpqo\nr1XFqqkscaZ9SXXh3x94drvPFC1p849HtfQOhmYO99mL8P2ZqvJVyvHsyz6FspX8DoVXN4nbrJTj\nIiJlkzUQMrPjzewPZrbazF40s5fM7MVyNE6KpFrvWle6LBfZk48/kXPq90z51nMY6IOgdNm9TKWd\nwZe1DrKEW4MO9XvttdfOt7/T2UX/OqOxofuqbc/d/M/b3+3ssX7bw83xDBiyBYOlfn81SnUhb/T+\nn6SvJYXzyQzEVaZ9SXXh38/8nEC5ZMNSZelSZcXKeTxb230W66glcOSTcPSS1FmtVPqyT6FU+wb+\nPAS/n3v0gwvf23ub1XSeSWmF53W6LK1UjVw69t8CfAVYCuzIsq6I5CLbZJytG1hw+Ee49903Ur79\nXray4KOHMfln16cObLNNZJow79HADn8F8cYb3Z+1S/86trztaGvvvn365jb/84D39OuxfuMrb2Sf\nWDQKhczzU4z3V6N0E35CwhihPCaHjWqC2VLItC+p5gn6yxb4/9u7/zi76vrO4+/v/NAhyRilBAhl\nsoYuSNAQWyYTrYu6tYgQxdhIQkUqLruO/ZFHseumtZRHf2jbR+Nu0dJuiSv+AtwQmgryMwhVm8oa\nMrGBKQYSS4RBQgAjYZIwkMl894/vPZk7555z7jn3nHPPufe+no9HHpe5c398zz2X5Ps5n+/383no\n0MzXCMtS1Otz5O2h+ucDrvpbT6Xpal6fZ1j57UcO1e875UnauynsO7jiBGnn4ei9Y+30PUN+Gumj\nhpYVZ4/QAWvt3dbaZ621P/X+5D4yoJ3Vydhs+K+/W1MdrtqUpEvv26QN3/tu8AOqMj7HVDcyrVoy\nOXj6mZKkjRs3HivQsGj+ayRJoz85cOzpu/a5idkvLXidpqamdMstt0iSBvvmRmefilIvGMz7+e0o\n6Ar+6FL3J8lV/Tiv26qTjqhjqa5Y5k3Yz52bTZbCm7ytf1p6+JDLMnUbacns/D5Pb8+N73+TXPck\nhX0Hv7Ko/t6xdvqeIT/sJesocTJC3zbGfFbSP0p62bvTWvuD3EYFtLuISfaGDRsqfYKmdckth7td\nB4/dPzU1pUsvvVSSaktnV2V8ZpR2rl5GV1kyuXzyD3TqwoXavXu37r//fp133nn6lTPn6f/9+0/1\nLz96Xg+NvaD9h1/RE/sP67hXdettp5+g++67T7t379aAeZUufM3J0hO7A4+lUMsWu+yUv2Ji0J6q\nPJ7frsKu4Kft5ZM0M1BmQccSdpX57sXZZCn8k7dJueVi587N73ON2q+T596bNN+VdvqeIR/sJeso\ncTJCy+Qaqv6FpP9V+fM/8xwU0PZCMjYbXmtd6eyqRhheYYRbexfqpndfPLOaXCUY2rBhw8x9Ouuu\nl+5eH6tIRk9Pj4aHhyVJw8PDeuaZZ7TynAH1H9ejl49M6df+9wO64ivbdOTolAZed5ze8Jqj04//\n5CfU846l0dmnJLLca5S2YmLQ82f1SeOHko9vbEK6fKd00vfcn8t3sua804RdZb5xXzZZiiImb2H7\ndaRkWS32Y7S+djqH7CXrKHX7CJUJfYTQNgKacdrZx+m9v9Svu/7p/mMPm1EdrtKsc8P3vlvTZ+jC\nX3mX7vjBuMyhlxpq7nno0CG9853v1MjIiBYuXKjrrrtOiwbfpj+/61Hdv3OfuozRr/3iKVra94w+\nueY3tWfPHi1dulTf/va3NXv/i9k0Fs2jQWnaPj/Vz190mnTr/dLhiWTjG5uQFm+TDvi2WM7tdkt6\n2nlZjr955YdPchP/Tmxm2WjPnLjNPxttTppG2B6hJP15Gmky22ravYlru53DVmrejFBZNlQ9SS4b\ndIq19gJjzFmS3mqtbfoGAAIhtJWASfrEvNdpxYoV2rx5s7q6unTTr650fYJ8k/jqpqvnn3++bl24\nTH3XfyNV49xnn31Wy5cvl/f/2Omnn66LL75Yxx9/vPbv369bbrlFu3e7JXBLly7VnXfeqXnz5oUe\nS+LgpezNfxsd35pd0v9+urbEtJH026c0d5lOMydk/slEj1y5nW5VNvAr3mSpXSaRl++Uvrqv9v6P\nnOT2t/glnVwWNXnzzs+WA653UZdxy/HinqciArhmarcgIUg7nsNWaN6MSHEDoTh7hL4i6cuSrqr8\nvEvSzXLV5FIxxrxH0ufl/mn8Iv2J0FG8suZV+iTdeuutWrlypS677LLavT8V3v033HCDNm3apL63\nfyT1xv4TTzxR3/nOd/S5z31O1113nXbv3q2/+Iu/mDnkgQENDw/ryiuv1OzZsyOPJbEt28tdnCBu\n8QT/xH3LgeCmj1bNXXPe7EpIQXtWqm+rNyCHTZY6uXpT1IbtoM8rqCJd3MlbmmAz7Z6bdt+PkfQ8\nhqk+R2fNcvf98HA5Lg604zlkL1nHiBMInWCt3WiM+ZQkWWsnjTGpy2gbY7ol/Z2k8yQ9JWmbMeab\n1tofpn1tQNJ0lmLrqNuT00iWogB9fX264447ZEKaqXouueQSrV692j0uo439s2fP1lVXXaXf//3f\n11133aWRkRGNj4+rv79fg4ODuvDCC9XTE+evjYTG9kqPBrQnK0NxAu979MRP3NXuqaosun98QRP3\nLuOyP/7ku1Fz15xnNSEL45+o3fHT+o0v602WgsY8Piktf1jq664/CUybTWr0+UHP++Hh4MfuDLm/\nkcll1OQt7FiaHWz6x3HWLPee/mxCu+zHyCJI8J+j6iWWZbg4sKy/vc8h2lqcGc0hY8zPqfLPuDHm\nLZIORD8lliFJP7LWPl553Q2S3i+JQAjp1evTk/a1cw6w6gVBNY+LUyUugZ6eHl100UW66KKLGnp+\nYuuul44GpE26TMPHkAn/96ha0GccNHHvsW5S8IovEnpNk/uX5HnVNmqiFqXXSItmuaU1QcFG0Jgn\nJY1WgoeoSWDaCX6jzx+b0OTZ39ed4/+ikaO7dHD7S5rzxdkafOuglvcsUs9k9/RjjdzxB8lychl1\nLHkHyPXGMavb/UnTh6rMsjiP/nNULc/zFRf9mVpDuywzzlicQOj3JH1T0i8YY74naZ6kD2bw3j8v\naazq56fkKtQB6eXVDDPPACsNry9Q2n06Rdk6Kk0GJJrPPK1cTVklyRhp3vHSqvNrP+OwifuSWdKb\n50h3V5rWXnC89OmFzf1HKM+rtlETNY8X2x/bI1RphHnr89OTYH+wETTmalGTwLQT/Aaef+jQIV1z\n8VVa/8LX9ZSec3dOSZqQ9O2v6lQzT8N6nz6hD2q2jnOXF2993k1Q/N+FJJPLehOcqGNp5rKmoHEc\nnpI+dKLU392e+zGyCBKiypRLxS9DS7MsE83RycuM66gbCFlrf2CMeYekN8j9U/aYtbbegoc4gi55\n11RuMMZ8TNLHJGnBAq4uIKa8mmHmFWBlIYt9OkUY2ytNvFx7f2+PdG5INa1mCfoeWSu9/pTgzzos\n2Mizl0sU/3K1vK6815uoSdK8Xumbb3JV47zJ0vhR6ev7woMN/yQySNgkMO0EP+Hzg4qNrFq16lix\nkY0bN2r37t26Wl/Sbfqe7tRf6kS9zgUCQcFV3MllnAlO1LE0c1lT2Dh2Hg6vnNfqsggS6l0QKMMy\nNPbUlFtemd82yDLVDYQqe3kulPT6yuPfbYyRtfavU773U5IGqn4+VdLT/gdZa78g6QuSqxqX8j3R\nKfJqhplXgNWpvAzb+KGZ9/d0p1ral5mk36MyLREJW4b0oZPcxDPLq7ZxJmqr5knL5ro/nqHt0cGG\nfxL50lE39hmlmkMmgWkn+Amef+jQoWNB0MKFC3X93/213nnkn2Se3Czt3yP1HNGffelPdf9Lb9Tw\n8LBG9jym5fqUvqNrNPvIceHBWZzJZeAEx7ePKmofTjO/s526lyRtkBB1QYBlaIgjj8xvm2SZ4jRU\nvV3S5ZJ+TlJ/1Z+0tkk63Riz0BjzKkmXyC3BA9JL20wzTEgj1MI39MeVZcPSLHgZNv+yuEW/UPxy\nQyn598ibuKdtjpmFsGVI/d3u6vu1Z2Q3rrULXEngoOaaURO1OI0LvUnk1nOkO8+W+qveJ+q1/WNK\nOmFM8PxrrrnmWBD0wAMP6D+/6RSZf71R+umPpEoD5K6XXtB5552nBx54QAsXLtSIHtPntCl9IBA4\nwZHbR7Vt3JU1vvV5FwQHHUszv7NBn+msLpcZbIdGnHnxn6OPnOT+FP13DFpHHk1io7JMLSROH6GH\nrbVn5/Lmxlwo6XNyq8a/ZK3986jH00cIiWTR2yboNbNu+tksZRz70GppW0A2bWixtPXm5o8nSB7f\no2ZopIFnGtV9N7wCAPUyT430vvH3rTFGentI35q4vUCiKqrVef7k5KQWLlyop556Svfee6/OO+88\n6cW90p3/XTrlzdL3/156ab/0tk9I5/2JJOnee+/V+eefrwGdqMdfe7N6Hn5L4xPZoB4ufr2mPPtw\nxiakq/e4/XJHrasCOKXp3lKzuqUVJ5SnNHRcbbBECG0sjz5jzf43JqEsG6r+laT7rbX3ZjW4RhEI\noRRadWJctoalY3ul5b8pje6aeX+Zmqi2smY2OUwzCWykcWGWTSpTvtZtt92mFStW6IwzztDOnTvV\n1eVbaHHNm6QDYzMCoampKZ155pnavXu3bvviLbroihT1h/zjD1M9OSly0h5nvF1ywVGrNB/thKap\naH1ZN4kteSPduIFQnKVx35f0DWPMS8aYF40x48aYF9MPEWhRXlGCrTe721YIgqRy7W/yslM7/33m\n/VktYUS8pV1jE+4fszTLkrxJ4Pqnp5diLRmJ/1re0rd/eKP7eeUj9ceS5ZKMsNe6ek+sz8a7OHfx\nxRfXBkEhurq6dPHFF7vnP/Fw8jFX8y+bWjyrdvdv9RIY//n6u6elhd+XLt8ZfIxZfEeqxakw6FXS\nb5WlNm2yRAhtrnqZcRZLo9MuPy6JOOWz/5ekt0oatfXSR0Cni+oxlKb/UBa9i/IqINGIsL1BZ54m\n3fn3rRNcllm9alVZbXTNohpR0rGEbfz98jPuv5Nc6Qx7rRv3uUuFdcZz8OBBSdLxxx8f7/0qvMeP\nj2dQ9rh6M37YEhhvcuI/X1bSUUk37JO++dOZx5jHZug4FQarFV0aOo5mliAHyqJNyqbHuXy1W9K/\nEQQhV2XbxN8IL8uxfqPb97J+o/t5bG/079K8bhJ5FZBoRFB2SpKOezVBUJairgBmdRU7bBK45UD8\nTELSsQRt/JWkQ1PJM1JBr2XkAoQY45kzZ44kaf/+/fHer8J7fH9/xGblRrIx9YofhAUiU6o9xnVP\nuj08/op0aTIdYedOCm6q0QpV5fLYiA60gqyzTAWIEwjtlfQdY8ynjDG/5/3Je2DoIFlN9PMYV5Lg\nLKrHUNTv6knz3Gpe09XhVS4LNLyquEIJrVh9rx2C9WpZXcUOmgT2SHr0cPzlcknHElWlrjpoiRNI\nBC3v8PaoxBjP4KBbgr5x40ZNTfmfFGxqakq33HLLjOfXSLPkMGpyEhWI+I9xy4GZpcol9/luOVB/\nDGFqPm9JrzbSktnSb5wkzQ2pbldmbbJECOhEcQKhPZLul/QqZVs+G3CymuhnqZHgLGwPzpYfSBvv\naXx/TpZ7e8qyv6lM2ak4yhisp927kdVV7KBJYLdxFcHSZHiixlKd9Zgd8M+Yl5GKE0gEZVA+fFLs\n8Sxfvlynnnqqdu/erfvvv3/6F+PPSPsfl6YqH8LEC+7niRd13333affu3RoYGNCFF14YfIz1smSN\nnn/vfAVlX6Tpin+Sq8oXJOz+OGo+71Ok3cukHUulryySRpeWo/x8EmUqmw8gkbp7hKy1f9qMgaCD\nlWkTvycqOAurZha2B+fRfw9eBhY3A1KmvT1Z8bJTrVJ9L+z7cPXfSP2z0+3dakQWezeyaqQZtE58\nywHpIV+T3HoZnqRjqd4XE1S5aMrG37vkb3g5NuH2y8QYT09Pj4aHh3X11VdreHhYDzzwgE6e93PS\nX58l9fZJXpZodKP00P/VyyeereG//JEkaXh4WD09If8MR2XJ0pz/gT7p7sXS+Q9LB45GP9aEREtd\nYVFUTFENRtM2Hy1Kq44b6HChGSFjzOcqt7cbY77p/9O8IaLtlXGZVCPBWVCWo8tIR6dqr6AaEz8D\n0krZkyTLx8qSnYoj7Ptw4+3FZImy2N+T5VVs/1Ksc+c2nuFJOpawZUldpvGlfwnH84lPfEKDg4Pa\ns2ePfvmXf1nfuu8+2Z5XS68ckiZfcg965ZA0OaF/2fqv+vGPf6ylS5fqyiuvDB9DVJYs6Py/MOkq\n3dUzNiFdMBoeBO08PP3fb59be7m0R+78AkAbCO0jZIw5x1q73RjzjqDfW2u/m+vIAtBHqE2VsdFn\noz13/D2G/nm79PBjtY878eekkY3Jq8aVOXtSxvOYlaDvgzFueVF1kNusHkglb2SXS/O+eu/nr1y0\n7smm9rh49tlntXz58mPltE8//XRdfPHFOv7447V//37dcsst2r17tyRp6YmLdee/3a958+ZFH1PY\nZ7jykeDz3y1pT53mrFENWP2fT6PnkeaiAAqWWUPVyovNkyRr7XMZjK1hBEJtrGwT/awm9WVrYpq1\n6rLeEy+7vkDVJbHb5ViDvg9TUy7b5ze02GW5Ql8rg0li0GS2R25/R193MU0y/cckFVtWtZnBWOX4\nDz3wrD732Nd03aFb9ZRq/7kc0Ika1vt05TmXa/bIf4r9ujWf4Zpdrv+P/5/vLkm/dUp0oBcWREvS\n63pqy2dfvUe6u1IR74LjpU8vrB8E0VwUQMFSB0LGGCPpjyX9jtx1zy65+jHXWmv/LMOxxkYghKbK\nIjgrY5Yki55E3utUH1uYeoFBq/B/H8YPSV+/M1mQm9Uk0f86PXK9YLrl/pZu5uSzzBPfRjqpJw1U\n/cdvpEl7VHfp+xrRYxrXS+rXcRrUG3Sh3qKe3p70WamxCdcENWh1W72sYFhGaPEs6c6zw3sIxT2v\neXabJ9MEIKYsAqFPSLpQ0sestXsq950m6e8l3WOtvSbD8cZCIISWVKZsV5aBWVC2y69dMkJBGvks\ns5wkVk/yXzrq9nZUn4ocl4HNEPeYWmES28jkPyywCCrBnWVW6vKdrglq9XvEOedxM2WNflfzWrZZ\n5oAbQOnEDYSi8d74DgAAIABJREFUqsb9hqTzrLXPe3dYax83xnxY0r2Smh4IAS3JKwpQBo1UwwsT\n1hTVU6+oQ1aZqbzUG18jle+y7EBfXaVqaHtAv5cmdbaPc0xZVLlrVJIALKoIRdjkP6xB6Qm90uv7\npstR7zw8/d8rH0kfDH56YezKdjPE7Qbf6Hd1Wb87v/4AKm1z0UbODQDUERUI9VYHQR5r7XPGmLB2\nbADKLGk1vKhgIKisd0+3tOgXpONeHR0Y+LMpO3ZKN91RnsIKcceXNMjNa5KY1+tm9d5FTWKTBmBJ\nJ/9jE9JEwPq0XiOtmlebEUsbDPqDursXSzfuS74PK06p50a/U1mVZffL8iICAFRENVR9pcHfASir\nJKXK6zURDSrr3T9buvPv65fELmMT3Wp5jS+vDvRFdraP895FTWKTlhlP0tjVC2yqy01LlSVbAZ99\n2pLn3vtVN4i9YNS9j1e2PCwIaqT5aqPfqbyai2bVABgAqkQFQkuMMS8G/BmX1MKdHNGWkvSvaUdx\njz9JT6J6wYC3NGx4lQukhlfFz+iUsYlutbzGl9ckscjO9nHeu6hJbNIALMnk3wts/EsSz5wV/Nmn\nDQYbDaSCAqglI/WDoTTfKX9fqSy+h0UG+wDaVujSOGttdzMHAjSs7MusomSxTybJ8SfZ1xInGGh0\n/1PQsrqimugGnYM8x5dXB/oiO9vXe++8lkvVk3R5V9z9M1L43qDjuoMfn2b54tiEtPG5eHux/Puh\n0ixLLPI75Zfk3ABATFF7hIDWkGUBgGbKKoBLevxxg5d6wUDcIC7ocWuvcMfqr7gWVlghL2Hn4O71\n5Rhfu8hrEluvEEIjAVjcyf9Zs4Kro3kFEfxj3HJA6jJSj62UOPeNJexYvIzOCyGFSbz3C9uDNPDq\nxjJRZazyV6bADEBbIBBC68trGVPeVc2yCuDyOv6oYCVuEBf1uKQV1/IQdg5uvL0c42snSSaxcSbh\ncYoPFJ1FCOr31G2kN86Szp1bG+wEHYuX0anX+zws82OtW06WJBNVZJU/AGgiAiG0vjyWMTVjuV1W\nAUxey7iiltGt+UxtAPGzF6Xlv+mKJXifUb1gr+iMXdQ5KFPZ804SdxIed8lXXlmEHx4Ovr+6eIJ/\njJNy7cnPnTtzTFHHErYEz/9+YXuQuozbW5MkK5Znlb8yZpoAdKyoYglAa0hSACCuZlQ1S1LBLUoe\nx+/xggF/FbiwHkKju6SF50mXf8oFk2UvipDVOWgl/gpiWw8kryiWp7hFAYoupxynAETUGKvPQ9T+\nn6D3CXq/sPGcOze86EFYNbm8PttGCzcAQE4IhND60lQvC9OMCXxWAUwex19PUADhOTolfe2bLqN2\n1mn1A40kFf+yrg6YZxBZRv6J6HVPS2/9V3dblolp3El40eWU41QxCxvjolkzz8NzASkf71i89/H/\n7+Yv0x01nqAqbkHfhdO3Sku2ud5INe+Xc1PUMmuk/DiAlsDSOBQr6T6csMdnvYypGVXNBua7Tfn/\n7Y+lx8ek0wak//On8QKYoM+hmcu4vP1DP3sx+PfWugyaJM3qk1485O4zxv3sBRpJliDmsVwxSRW9\ndhC0VKv6tlmNTqPEra5WVCU6T5z9R2FjlGaeB2//T5ekKdUGMd77bDkgTVWWu1XvMYo7nmpB34VJ\nKz18yM0MjsrdBhV2aFScILdsS+fqLdUs23gBJGKsrbcDszwGBwftyMhI0cNAVvwTW+9qfNjENunj\nmzm2Zr5HMz+HeuNf/ptuOVyYJW+QfvwTafzQ9ASuf7Y0etv0XqP1G2sDzuFVtYFdkscmPY48i2KU\nyZJtbqJbz1C/yx4UwT/x9CbhQRv1vUlomcspB41x5SPBFedO7JVe39ecYxnaHjwGT69cT6TjutON\npzpQmDjq9jRVJ9t7jVuud+0ZAedeLstVZJGGNbtc1swfmA/Pd59J2cYLQJJkjNlurR2s9ziWxqE4\nSffhNGPfjifJcrNGl2s1ejxZfg5plpoNzHeFEV73Ghfg+PX2uODn8IS7laZ/9saaZAli2GM3bm58\niZwXVK7fKG0bdbdLPtCeDXnHJqTHQjb4V2t0CVRWy4eSNPLMo3Fn1oLGGLZkbtW86cdJ+S7Hitp7\nJLmJ/XHd6T5b//K7nYenM01SbaapjEvnorJYZRwvgERYGofiJN2H0+yN93GW26VZrtXo8WT1OWSx\n1MwLGK/+G1dyespWyvVWslRdJnqsSZYgBj1Wkp7b744jzrj92Z/xQ7VB5Qvj7ni+8pfxPoNWse5J\n6WjICoC0S6CyLrfc7v1i6i3ra0b5av8Y/PLYEzSp6ExT0QUwgkQt1SzjeAEkQkYIxUlasauMFb7S\nZGcaPZ6sPoesMksD813QsOdb0m//+swM2rnn1I61y0iLTnP/naRYgfdYf/bJ249Ub9z+7M91N0tf\nva02sLLWBXXtlhXaOj5zSZJn0XHSx2NkX6K04pXxqAxW3pvj62W9mvF5Vo9hyWzp1SY8U9OowEBB\n4ZmmogtgBIkqQlHG8QJIhD1CKE6ee4Sate9jaLWbVNfcv9iVnI5S9B6hJGNv9PMc2ystfr904ODM\n++fOmd4n5L12nGIFY3ulwYulZ/fHG3e1oD1GYbqM9Fu/3l59hKL2OqTNvoTtNylyr1GUqL0oUvH7\nPor4PPPYb5X0O5dkf1gzhX02ZR0vgNh7hFgah+IkrdgV9/Fxlnx5k+8t291yLmOkt5+TPGBKU12u\n0YplWVU6izv2NEvoBuZLK97lymlXX3Tx9gl5vYniBhwD86VV7wkumlDvMw/rfRRkypan11FW8qyy\nFrfSW1nUy7jk1Uw0riI+zzyWIyb9ziWtfNcsYZ9NWccLIDYyQmg/9aqLeRP78UPS5NHpx/R0u4pm\nSTIrZang1oi4Y09brS1N1qyRcYdlr5JkhNJWoytrSd28qqy12pXxqIyLVfHZLe/zHJ+cXs74aiN9\n983SsrnxX+PqPdLdlezpBcdLn17Y/PPRCpX9ALQdMkLoXPWKCXh7Y6qDIMn97O01SZKhaNU+NHHH\nnrY4Q9Y9maLGHZW98nofVe+LCpK2qWrQRvcb9kkrTpB+eLjYwCivIgTNvjKeNtCsl3HJOxtTb/wD\nfdLdi6V37HC9fSRX6OKC0XjB5diEtHibdKDq77iv7pNufV4aXdrc7167F74A0NIIhNB+6k28o5ZI\nNVJ9Letmrs0UZ+xpAxl/AJI20Igad1QBiGv/aDqA2rJdevRx6eiUC4B7e9y+oDNPcwUe0gSzQcuu\nDhyVvrbPZRvyqABWBs2a8GZRUa3ekq16Fd3SBGFxx3/jvumy85LLDMVdorfuSWn8aO39L6ZY4pd3\nlrOsWVQAbY2qcWg/9SqRBVVd8xRdhS6uNP1/kkpS2S1Ikp5MadXLXnkB1I5vSLvvkT6+enpMu+9x\n93v7lhoeQ0ClLMkFQVJrVFQrsywqqkVVbYv6nb8vzvqn3c9JqsrFHX+a0sxbx6WpgPutgp9fr0pe\nFscdJe/XB4AQZITQfuot+fIyFEF7hNJmKpohi/4/SWSx/K9ZWbMk2au8xhS07MqPXiONy6p3S1QG\nK+x3UUFM3CxL3PGnKZiwrF/aHhAMGdU+P06GKovjjpL36wNACDJCaE/eJHfrzbVX+L2J/cdXS0vO\nlBafIS15g/u5FYoc1Ov/k0e2KOrzbLao40ubvcqCv+9I0N+yZa6oVnZF9m7JIgiLO/6o/jX1rF0g\n9XfX3v+agOfHyVDl3TiUxqQACkJGCJ2plff1RC3/ana2qNnqHV8Zilf4CwcsmuU2qR8+mn3Z6nYS\nd49InmXA68mirHXc8Vd/j7YccPuFuoz7ud7+mYE+VxQhqmqc93l/+ZnoIGRsQpoI2G+UZfDZauXX\ni8Z+KiAzlM8GWk1UOWspXalrT7Ma0iaVtpR3USghHC2qwWnQ51TU55lVmfAk40/62TRyHH5e09O1\nC2rLeCujMUSNp+zl14uUx/cBaEOUzwbaVVQVtpVXpit1LZU7q5S2lHdRKCEcLekekaI+z6zKhCcZ\nfx77Z/yvWa06Q+U9zl9k88xZ0p1nZzfxpjFpfOynAjJFIAS0mqjlX1n07KlXgrpRWWSZsu5JhHJo\npT0izSwTHmfpWiPCKhvO7pI+evJ0EBL2uOO6sw9SuFgQTyv9vwK0AAIhIExZl4dJ4XucsujZk0fW\nJassUx49iVA89ojMFGfpWprPJuzz/ujJM4MRzkv5cE6ATFE1DgjiTdzXb5S2jbrbJR/It19PFrLo\n2RPUZylt1qVepbu4mtmTyFOvxwrSS1MhrR3FXbrWiLEJ12x1Sq6cdtRrcl7Kh3MCZIpiCUCQVt2U\nnwV/9sbLuqQJOIZWu4Cy5v7FriR3VrLI4lW/xlnnSLe+RTo8xcbkvFFQYtrQdtdY1M+/dC0pf6ap\nSy4Y+vBJMyvK+Z/DeSkXzglQF8USgDRadVN+FvIoQd2MvT1ZLL/zv8bIGyX7io79VXlE0guT0uB2\nadW82gkIZW0bV8Y9InHOZ5bn3HutJyZcgFJ9nTJo6VrSY5g4OrMC3FTldfsj9vyU8bx0Os4JkBkC\nISBIp2/Kz7rPUjP29mRR5MH/GnaBav6atJKePSKtf9r1g/GyQ/6r7TvGZ/4erSXO+Yz7mDiBUtS+\noEaXP9XbaySx0R5AR2OPEBBk7RVuou7tlWFTfjrN2NuTRRav5jV+rNrawd5ra7psrRRd1hatJ875\nrPcYLxBZ/7Rb6rb+afdz0D6zoH1BXZJO7HU9fRoJqKP2GnnYaA+gg5ERQnxlrqKWtTyWh3W6rLNM\nfllk8Wpe41uShqSuLmkq4LpR9dV0ytq2lzjns95jkvR8CXqtKUmvT7EMKqz8tadLbLQH0NHICCGe\nVq2iloY3cd96s7slCCq3LLJ4Na8xLs39vHTZ8e7KvP9vzOqr6cv6pys5Bf0erSXO+az3mCTBcR7f\nn6DXrHZCL0s3AXQ0AiHEk1X5YyAvWSy/C3qN0a9IX3mzNHKONDeibG2csrZFl+Iu+v1bSZzzWe8x\nSYKbPMoie69pAn7Xa1zBD4IgAB2M8tmIp1nlj9FakiyXbIellfXK1kb93r9xvdmluIt+/1YUdj6r\nCyCcNcs9dufhGOe8EtyEfeZ5lEUem5Cu3iPduM8V+vAqxUWNAwBaXNzy2QRCiKeT++ogWJJ+Q3n0\nJmo1a3a5zfL+jvDD85tTCrfZ79+upcSTBpRl6flSlnEAQBPEDYRYGod4qKIGvyTLJZM8dmyvC7yH\nVrvbdtmHVnQxhWa+f5Jqaa0maXVAr+fL1nPcbbOyf/4lkEWMAwBKjqpxiIcqavBLUq467mPDmqLe\nvV668fbWXla3rN/1mfFnZJpVTCHN+yfN7iSpltZqig5o66GfFQDERiCE+PIuf4zWkqRcddzHBmWO\nxg9J7/gNacrODI5abVnd2gVuQurfL9Ks0sX+9++RWxOw5YDLGoTtd9pyQHr0sHTUupZKcSbWWQQL\nZV1aV3RAW0+ZgtCynkMAqGBpHNCp0i5BS7JcMu5jgzJHk0ell4+0fsXCgT4XPAyf4ibNjTbJzOL9\nl8yWuo0Lbh46VLt0rXpp20OHpJftdF/ZOI1i05aCLvPSujyqu2WpLBmrMp9DAKggEAI6URZ9oZKU\nq4772GWLp4OlKGFL8Mqu6H0a3vufO9dl2MKCG39Wwa/exDptsJB0H04zFR3Q1lOWflZB5/Bnk9Ly\nhwmGAJQGS+OAThRVvCDJ8sckyyXjPHbtFW7ZW3V1uS4jHZ1ymSFP2BK8IO1Qtjtr9bIGQb+vVm9i\n7QULjVYpK0tWI4wXUJZR0UswPWHfodHDLjNUpuARQMciIwR0oiSFDpopKHP03a9J/bMbq1iYRear\nHdXLGpw1K/xfh7gT6zTZr6KzGq3ceLYsGaugc+gpS3YPQMcjIwR0oiSFDpotKHPUaMXCrDJf7SYq\nazA2Id36vGu8We1VRlo0yy2ry3vTe5FZjbEJafE26cWjrgHpyLh0wz5pdGnrZDDKkLHyzuHPJmt/\nV6bsHoCORiDUCJbaoNUFLUErc1+oRisW5pn5auWKWFFL1y7f6YKAakbSr58ofWVR8ePL29V7pANV\nx2/lfr56T/OOvx1453D5w245XLUyVdkD0NEIhJIK63PSaqV80dk6pS9UXpmvdujVEpQ1GJuQbtzn\nJv/VrKSdvsms9/i8gsFmZTX8x3DHT4Mfd/f+/MfSbgb6pDvPnvn/Stmq7AHoaARCSbHUBu2iE/pC\n5ZX5KlOvliyte7I2CJJcRsh/Bb9Vg8HqwOesWW4Z4OGj08cQsJIr8/dNGDRaa2WMyexxTVVkdi9L\nrZwBBhCKQCipsm4yB1Arr8xX2auaNWrreO3eIMkVTvBfwW/FYNAfvI2Mzwz8jsgFfUEuOD67900Q\nNE5MTGjlypW67LLLdMkll4Q+bsOGDbrhhhu0adMm9fWVbIJehj1LabRq0A+grkKqxhljPmuMedQY\n87Ax5hvGmNcWMY6GBPU5KcsmcwC1vMzX1pvdbRbL/4quapaXoOPqkvThk2onfK0YDPqDt6Dsl5XU\nrel/Hbskze2WPr0wu/eN2RdpYmJCK1as0F133aVLL71UGzZsCHzchg0bdOmll+quu+7SihUrNDHR\nQlXuWkGZ+1oBSKWo8tnfkvQma+3ZknZJ+lRB40hu7RVuaU0jpXwBtK7qksrjR6VZ3Y03DC2rtQvc\ncVUHAf0hQUCcYDCvMtSNvm69/kiSO4YPnyT9VqX89G+dkr5iXANBo7VWK1eu1ObNmyVJU1NTgcGQ\nFwRNTblU3ubNm7Vy5UpZGxTl1dHKZcPz1IpBP4BYClkaZ629t+rH70v6YBHjaEinbDIHMC1oacys\nbulDJ7kiAkXve8h6/4L13QapV+I6r+VEaV53Wb97vH9S2yW3JNA7hk8vzPZcBr1vnQyiMUaXXXaZ\n7rnnnmNBjhcMSdIll1xSEwRJUldXly677LLke4VY/hWugfMHoDWYhq4aZTkAY26XdLO19saQ339M\n0sckacGCBec88cQTzRwe0Foo7Z6PNbuk9U/XToSG5xe/98E/ge2VNKensQls0uP0ArCgTfB5fWZp\nXrfmszLSrC5pxQn5BrRB7zunO9Y5Cgx2TJfeN/dc3X5gi6bszCDopptuitxLFKrM3/GipTh/AIph\njNlurR2s97jcMkLGmPsknRzwq6ustbdVHnOVXI2em8Jex1r7BUlfkKTBwcFiozagzCjtnp8yL43J\nsmhB0uOM2gSf12eW5nWzrmAWNxOX4n29oKY6GJqyU7rthe/OeFxgEJQkU1jm73jR2qXyHYAauQVC\n1tpfjfq9MeYjkt4r6V226LQU0A4o7Z6fMi+NyXICm+Vxxn2tpMv60o4xqwpmSZeSpXjfY8HQhy6d\nkQHydJmQICjJ+Mr8HS+DVq98ByBQUVXj3iPp9yVdZK0N6NKHtjK2V1rzGWlotbsd21v0iNoTpd3z\ns3aBW25WxuIIWVawy/I447yWN1lf/7S0bdzdLhmJ3qRflnPR5Epil1xyid4399zA371v7rm1y+GS\njq8snysANFFRVeP+VlK/pG8ZY3YYY64raBzIm7dca/1Gaduou13yAYKhPFDaPT/e0pjhSiWx4fnl\n2R+Q5QQ2y+OM81qNBBNlORdNXkq2YcMG3X5gS+Dvbj+wpba0diPLHMvwuQJAExVVNe4/FvG+KECc\n5Vps8M/G2ivcniDv86a0e7bKujQm6/0LWR5nvddqNJgow7lo4lKyYwUTApbFSW7PUHU1uYbHV4bP\nFQCaqKiMEDpFveVaZIyy45V2H17lskDDqyiUULRm9WXxJrBbz3G3rXIVv5Ub0zZpKVlY1bj3v/Yd\n6jLT/4TX9BliqRsA1FV4+ewkBgcH7cjISNHDQBJrPuOCm+pgqLdH+tByqX+2tPEe6fmfSVN25u+H\nV7HBH60ty7LW7arVyxJHlQ/PQFifIK8wQr3f5z0+ACiruOWzCYSQL39J594eaVblH+LDE7XZIs/Q\nYmnrzc0bJ5A1+rLEw2Q9kLVW733ve3XXXXcduy+oRHZQMHThhRfqjjvuSN5UtayybhgMoO0V3kcI\nkDS9XGvd9W453NBiafyQ9PU7w4MgNvijHdCXJR72pQQyxmjTpk1asWKFNm/eHNos1d9n6Pzzz9em\nTZvaKwhKUgYcABIgEEL+BubPXOY2tDo6CGKDP9oBfVni44p/oL6+Pt16661auXKlLrvsstoS2RXe\n/TfccIM2bdqkvr42+uziNAzm+wOgQSyNQ/MF7RsyRpp3vLTqfKrGoT2E7X+5e7F04z4mbR72UtVl\nrY2V4Yn7uJYytN31mKq5v98VB+H7AyBA3KVxVI1D8629wmV9vJ43vT3Sa/ulkY0uc0QQhHYQ1Jfl\n7sXSBaPJGoi2uywbk6ap0tesCn8NiBvctF0QJNWvLNjkxrYA2gtL49B8QfuGyAKhHfn3v6zZVX+Z\nT6fJai9Vmr0k7EMpr7UL3LnwZ1a9MuDsxQOQAhkhFMPbN7T1ZrJArWxsr1vqOLTa3dL/KRqTtlpZ\n9RJKkxkgq1BeQZnV6gC1lXtRASgcGSEAjfGXRt+xU7rpDpq4RqGAQq16V/zjShNkEqCWW1Rlway+\nPwA6EhkhAI1Zd/10ECS524OH3f0ItnaB28jtXcFm0lb/in9caTIDZBVaV1bfHwAdiapxABoztFra\nNhpwP81wI9FANB9hVfoa2SOU5LkAgNKhoSqAfC1b7JbDVZdBpxlufTQQzYeXGWgkyEzzXLQ++hAB\nHYuMEIDG+PcIec1w2SMEoFXQhwhoS/QRApAvrwz68CqXBRpeRRCE7JS4rw/aCBUDgY7G0jgAjfPK\noANZoq8PmoWKgUBHIyMEACgXrtKjWagYCHQ0AiFEo2EmgGbjKj2ahZL2QEdjaRzC0TATrWhsr+tl\ntHXUVbZbewXf1zjKVDmLxrNoFioGAh2NqnEIt+Yz0vqNteWRh1exLwTlRCW7xuRROStNYEVfHwBA\nClSNQ3pbR2cGQZL7+cGAJppAGay7fjoIktztwcPufoTLek+OF8isf1raNu5ul4zEr/zmXaUfPsVd\noR+eTxAEAMgcgRDCLVvsrqhXo2EmyozgvTH19uQkLWWdRWDlNZ7deo67jQqCKLUNAGgAgRDCrb3C\nLSvygiFvmdHaK4odFxCG4L0xUZWzGsnuNLPYQdzxESwBAHwIhBCOhploNQTvjYmqnNVIdqeZJYnj\njC/tUj0AQFuiahyi0TATrcQL3tdd75bDDVE1LpaoylmNZHfWLnANUP3FDvIoSRxnfFHB0rVnZD8m\nAEBLIBAC0F4I3hvj7cnxa6SUdTNLEkeNz6tc9+Vn6EsEAKhBIAQACNdodicssGrW+D580swS3H70\nJQKAjsceIQBAuLKXsg4b3437ooOgvJbqAQBaBhkhAEC0ZmV3GhU0vqC9Q5I0u0v66Mn5LdUDALQM\nMkIAgObLu5x1WOW6j55cvy8RAKAjkBECADSXV87aW7q2Y9zt88lyyV0zK9cBAFoSGSEAQDbiZnka\n6U2UVNn3NgEACkdGCACQXpIsTyO9iRpR9r1NAIBCkRECAKSXJMsTtn+HctYAgCYiEAIApJcky7N2\ngTSnZzoYYv8OAKAABEIAgPSSZHnYvwMAKAH2CAEA0ktapY39OwCAgpERAgCkR5YHANBiyAgBAKKN\nTbiiB1vH3RK4tQuCAxyyPACAFkIgBAAI14zmpwAAFIClcQCAcM1ofgoAQAEIhAAA4ZrV/BQAgCYj\nEAIAhKP5KQCgTREIAQDC0fwUANCmCIQAAOEoiw0AaFNUjQMARKMsNgCgDZERAoAyGJuQ1uyShra7\n27GJokcEAEBbIyMEAEWjVw8AAE1HRggok7G90prPSEOr3e3Y3qJHhGagVw8AAE1HRggoi7G90pIP\nSAcPS0cmpR07pZvukB76hjQwv+jRIU/06gEAoOnICAFlse766SBIcrcHD7v70d7o1QMAQNMRCAFl\nsXV0OgjyHJmUHhwtZjxoHnr1AADQdARCQFksWyz1+lar9vZIQ4uLGQ+ah149AAA0HXuEgLJYe4Xb\nE+Qtj+vtkebMcvej/dGrBwCApiIjBJTFwHxXGGF4lcsCDa+iUAIAAEBOyAgBZTIwX7r2j4oeBQAA\nQNsjIwQAAACg4xAIAQAAAOg4BEIAAAAAOg6BEMKN7ZXWfEYaWu1ux/YWPSIAAAAgE4UWSzDGfFLS\nZyXNs9Y+X+RY4DO2V1rygelSzjt2utLOVDEDAABAGygsI2SMGZB0nqQnixoDIqy7fjoIktztwcPu\nfgAAAKDFFbk07hpJayXZAseAMFtHp4Mgz5FJ6cHRYsYDAAAAZKiQQMgYc5Gkn1hrHyri/RHDssVS\nr2/lZG+Pa/QJAAAAtLjc9ggZY+6TdHLAr66S9IeS3h3zdT4m6WOStGDBgszGhzrWXuH2BHnL43p7\npDmz3P0AAABAizPWNndlmjFmsaT7JR2u3HWqpKclDVlrn4l67uDgoB0ZGcl5hDhmbK/bE/TgqMsE\nrb2CQgkAAAAoNWPMdmvtYL3HNb1qnLV2VNKJ3s/GmB9LGqRqXAkNzJeu/aOiRwEAAABkjj5CAAAA\nADpOoX2EJMla+/qixwAAAACgs5ARAgAAANBxCIQAAAAAdBwCIQAoytiEtGaXNLTd3Y5NFD0iAAA6\nRuF7hACgI41NSEtGpIOT0hFJO8alm56VHhqUBvqKHh0AAG2PjBAAFGHdk9NBkORuDx519wMAgNwR\nCAFAEbaOTwdBniNWenC8kOEAANBpCIQAoAjL+qVe3329RhrqL2Q4AAB0GgIhACjC2gXSnJ7pYKjX\nSHO63f0AACB3BEIAUISBPlcYYfgUlwUank+hBAAAmoiqcQBQlIE+6dozih4FAAAdiYwQAAAAgI5D\nIAQAAACg4xAIAQAAAOg4BEIAAAAAOg6BEAAAAICOQyAEAAAAoOMQCAEAAADoOARCAAAAADoOgRAA\nAACAjkNk0V1pAAAI80lEQVQgBAAAAKDjEAgBAAAA6DgEQgAAAAA6DoEQgGhje6U1n5GGVrvbsb1F\njwgAACC1nqIHAKDExvZKSz4gHTwsHZmUduyUbrpDeugb0sD8okcHAADQMDJCAMKtu346CJLc7cHD\n7n4AAIAWRiAEINzW0ekgyHNkUnpwtJjxAAAAZIRACEC4ZYulXt8K2t4eaWhxMeMBAADICIEQgHBr\nr5DmzJoOhnp73M9rryh2XAAAACkRCAEINzDfFUYYXuWyQMOrKJQAAADaAlXjAEQbmC9d+0dFjwIA\nACBTZIQAAAAAdBwCIQAAAAAdh0AIAAAAQMchEAIAAADQcQiEAAAAAHQcAiEAAAAAHYdACADawdiE\ntGaXNLTd3Y5NFD0iAABKjT5CANDqxiakJSPSwUnpiKQd49JNz0oPDUoDfUWPDgCAUiIjBACtbt2T\n00GQ5G4PHnX3AwCAQARCANDqto5PB0GeI1Z6cLyQ4QAA0AoIhACg1S3rl3p99/Uaaai/kOEAANAK\nCIQAoNWtXSDN6ZkOhnqNNKfb3Q8AAAIRCAFAqxvoc4URhk9xWaDh+RRKAACgDqrGAUA7GOiTrj2j\n6FEAANAyyAgBAAAA6DgEQgAAAAA6DoEQAAAAgI5DIAQAAACg4xAIAQAAAOg4BEIAAAAAOg6BEAAA\nAICOQyAEAAAAoOMQCAEAAADoOARCAAAAADoOgRAAAACAjkMgBAAAAKDjEAgBAAAA6DgEQgAAAAA6\nDoEQAAAAgI5DIAQAAACg4xAIAQAAAOg4BEIAAAAAOo6x1hY9htiMMc9JeqLocSATJ0h6vuhBIDec\n3/bG+W1vnN/2xbltb5zfaf/BWjuv3oNaKhBC+zDGjFhrB4seB/LB+W1vnN/2xvltX5zb9sb5TY6l\ncQAAAAA6DoEQAAAAgI5DIISifKHoASBXnN/2xvltb5zf9sW5bW+c34TYIwQAAACg45ARAgAAANBx\nCIRQOGPMJ40x1hhzQtFjQXaMMZ81xjxqjHnYGPMNY8xrix4T0jHGvMcY85gx5kfGmD8oejzIjjFm\nwBjzbWPMTmPMI8aY3y16TMieMabbGPOvxpg7ih4LsmWMea0x5h8q/+7uNMa8tegxtQICIRTKGDMg\n6TxJTxY9FmTuW5LeZK09W9IuSZ8qeDxIwRjTLenvJF0g6SxJv26MOavYUSFDk5L+u7V2kaS3SPpt\nzm9b+l1JO4seBHLxeUn3WGvPlLREnOdYCIRQtGskrZXEZrU2Y62911o7Wfnx+5JOLXI8SG1I0o+s\ntY9ba1+RtEHS+wseEzJird1rrf1B5b/H5SZRP1/sqJAlY8ypkpZL+mLRY0G2jDGvkfR2SddLkrX2\nFWvtC8WOqjUQCKEwxpiLJP3EWvtQ0WNB7v6LpLuLHgRS+XlJY1U/PyUmym3JGPN6Sb8oaWuxI0HG\nPid34XGq6IEgc6dJek7SlytLH79ojJld9KBaQU/RA0B7M8bcJ+nkgF9dJekPJb27uSNClqLOr7X2\ntspjrpJbdnNTM8eGzJmA+8jkthljzBxJmyRdaa19sejxIBvGmPdKetZau90Y886ix4PM9Uj6JUlr\nrLVbjTGfl/QHkq4udljlRyCEXFlrfzXofmPMYkkLJT1kjJHcsqkfGGOGrLXPNHGISCHs/HqMMR+R\n9F5J77LU6m91T0kaqPr5VElPFzQW5MAY0ysXBN1krf3HoseDTL1N0kXGmAsl9Ul6jTHmRmvthwse\nF7LxlKSnrLVeFvcf5AIh1EEfIZSCMebHkgattc8XPRZkwxjzHkl/Lekd1trnih4P0jHG9MgVvXiX\npJ9I2ibpQ9baRwodGDJh3BWpr0rab629sujxID+VjNAnrbXvLXosyI4xZouk/2qtfcwY8yeSZltr\n/0fBwyo9MkIA8vK3kl4t6VuVrN/3rbUfL3ZIaJS1dtIY8zuSNkvqlvQlgqC28jZJl0kaNcbsqNz3\nh9bauwocE4D41ki6yRjzKkmPS/poweNpCWSEAAAAAHQcqsYBAAAA6DgEQgAAAAA6DoEQAAAAgI5D\nIAQAAACg4xAIAQAAAOg4BEIAgNSMMUeNMTuMMY8YYx4yxvyeMaar8rtBY8zfFDSuBzJ6nYsrxzZl\njBnM4jUBAMWifDYAIDVjzEFr7ZzKf58o6euSvmet/eNiR5YNY8wiSVOS1ss1oxwpeEgAgJTICAEA\nMmWtfVbSxyT9jnHeaYy5Q5KMMX9ijPmqMeZeY8yPjTG/ZoxZZ4wZNcbcY4zprTzuHGPMd40x240x\nm40x8yv3f8cY81fGmAeNMbuMMedW7n9j5b4dxpiHjTGnV+4/WLk1xpjPGmP+rfJeqyv3v7Pymv9g\njHnUGHOTqXQA9h3TTmvtY834/AAAzUEgBADInLX2cbl/Y04M+PUvSFou6f2SbpT0bWvtYkkvSVpe\nCYaulfRBa+05kr4k6c+rnt9jrR2SdKUkL+P0cUmft9a+WdKgpKd87/lrkt4saYmkX5X0WS+4kvSL\nldc6S9Jpkt7W6HEDAFpHT9EDAAC0rZrMSsXd1tojxphRSd2S7qncPyrp9ZLeIOlNkr5VSc50S9pb\n9fx/rNxurzxekv6fpKuMMadK+kdr7W7fe/4nSf/XWntU0j5jzHclLZX0oqQHrbVPSZIxZkflNf8l\n6cECAFoLGSEAQOaMMadJOirp2YBfvyxJ1topSUfs9GbVKbkLdEbSI9baN1f+LLbWvtv//Mrr91Re\n6+uSLpLLKm02xvyKf0gRw3256r+PvSYAoL0RCAEAMmWMmSfpOkl/axuryPOYpHnGmLdWXq/XGPPG\nOu95mqTHrbV/I+mbks72PeSfJa02xnRXxvd2SQ82MDYAQJsgEAIAZOE4r3y2pPsk3SvpTxt5IWvt\nK5I+KOmvjDEPSdoh6ZfrPG21pH+rLG07U9LXfL//hqSHJT0k6Z8krbXWPhN3TMaYDxhjnpL0Vkl3\nGmM2x30uAKCcKJ8NAAAAoOOQEQIAAADQcQiEAAAAAHQcAiEAAAAAHYdACAAAAEDHIRACAAAA0HEI\nhAAAAAB0HAIhAAAAAB2HQAgAAABAx/n/l8TTM3YOqacAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13887bc9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.cluster_results(reduced_data, preds, centers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data recovery\n",
    "\n",
    "Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the averages of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to the average customer of that segment. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code block below, you will need to implement the following:\n",
    "\n",
    "* Apply the inverse transform to centers using pca.inverse_transform and assign the new centers to log_centers.\n",
    "\n",
    "* Apply the inverse function of np.log to log_centers using np.exp and assign the true centers to true_centers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Segment 0</th>\n",
       "      <td>3552.0</td>\n",
       "      <td>7837.0</td>\n",
       "      <td>12219.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>4696.0</td>\n",
       "      <td>962.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>8953.0</td>\n",
       "      <td>2114.0</td>\n",
       "      <td>2765.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>732.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "Segment 0  3552.0  7837.0  12219.0   870.0            4696.0       962.0\n",
       "Segment 1  8953.0  2114.0   2765.0  2075.0             353.0       732.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_centers = pca.inverse_transform(centers)\n",
    "\n",
    "# Exponentiate the centers\n",
    "true_centers = np.exp(log_centers)\n",
    "\n",
    "# Display the true centers\n",
    "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n",
    "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n",
    "true_centers.index = segments\n",
    "display(true_centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average purchase costs for each category:\n",
      "Fresh               12000.297727\n",
      "Milk                 5796.265909\n",
      "Grocery              7951.277273\n",
      "Frozen               3071.931818\n",
      "Detergents_Paper     2881.493182\n",
      "Delicassen           1524.870455\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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PjXmm6K/AQRGxc/Zs0BDgZeA3wAlZnzHAw9nnR7Jtsv0zU0opax+drU7XHegB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\nRZIkSZJy7f8BYonm8/Du9e0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13887c12d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Average purchase costs for each category:')\n",
    "print(data.sum() / 440)\n",
    "\n",
    "# Visualize samples\n",
    "true_centers_bar = true_centers.append(data.describe().loc['mean'])\n",
    "_ = true_centers_bar.plot(kind='bar', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 0 has a high demand on Fresh category, though it has a lower than average for every category. It should best identify as  the small fresh produce markets.\n",
    "\n",
    "Cluster 1 has a higher than average demand on Grocery, Milk and Detergents Paper categories. It should best identify as the retailers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 0\n",
      "Sample point 1 predicted to be in Cluster 1\n",
      "Sample point 2 predicted to be in Cluster 0\n"
     ]
    }
   ],
   "source": [
    "for i, pred in enumerate(sample_preds):\n",
    "    print(\"Sample point\", i, \"predicted to be in Cluster\", pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My initial assumption for Sample point 0 is not consistent with it's predicted classification of Cluster 0. It has a very similar distribution of needs as Cluster 1, where your typical fresh produce market has Fresh foods as their primary product, and a small selection of Frozen foods.\n",
    "My initial prediction for Sample point 1 is completely consistent with it's predicted classification of Cluster 0. It has a very similar distribution of needs as Cluster 0, where your typical cafe or restaurant needs a good supply Milk and Grocery ingredients, and a good supply of Detergents_Paper to clean & sanitize the place and provide paper napkins for their customers.\n",
    "It's hard to say whether my initial prediction for Sample point 2 is consistent with it's predicted classification of Cluster 0. Your typical deli inherently needs a lot of Delicatessen foods and some need for Grocery and Fresh foods. Neither clusters put too much importance on Delicatessen foods, and while it's Grocery needs matches Cluster 0, it's Fresh foods needs better matches Cluster 1. We can see it's on the border between the two clusters in the cluster visualization (after Question 7), so we can't have much confidence in which cluster it belongs to either way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
